A machine learning-based enterprise operation data analysis method

Abstract

The application relates to the technical field of data analysis, and discloses an enterprise operation data analysis method based on machine learning, which comprises the following steps: performing cross-source caliber mapping cleaning processing on original data to be cleaned of an enterprise to obtain a standardized operation data set of the original data to be cleaned; performing high-order feature extraction on the standardized operation data set to obtain an initial operation feature set of the standardized operation data set; performing dynamic weighting screening on the initial operation feature set to obtain an operation feature optimization set of the initial operation feature set; performing mode refining on the operation feature optimization set to obtain an operation analysis processing benchmark of the operation feature optimization set; and performing diagnosis deduction on the standardized operation data set based on the operation analysis processing benchmark to obtain an operation analysis report of the standardized operation data set; and the application can improve the efficiency of enterprise operation data analysis.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a machine learning-based method for analyzing enterprise business data. Background Technology

[0002] In the current process of enterprise operation data analysis, the processing of multi-source raw data only adopts fixed standard execution caliber mapping and data cleaning. It is impossible to complete the semantic matching and synonym field aggregation of cross-source fields based on the enterprise business metadata dictionary. The data standardization processing effect is poor, making it difficult to form a unified and standardized operation dataset, and the data quality cannot meet the needs of subsequent analysis.

[0003] Current business analytics technologies employ static weighting mechanisms in feature selection, failing to leverage historical business cycle data for dynamic weighted feature selection. This results in low correlation between feature selection results and actual business operations. Furthermore, the lack of a dedicated analytical benchmark based on DuPont analysis logic limits business diagnostics to simple difference quantification, hindering comprehensive multi-dimensional analysis of business trends. Consequently, analytical conclusions lack comprehensiveness and accuracy, failing to provide effective support for business decision-making. Therefore, improving the efficiency of business data analysis has become a pressing issue. Summary of the Invention

[0004] This invention provides a machine learning-based method for analyzing enterprise business data to address the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides a machine learning-based enterprise management data analysis method, comprising: S1. Based on the enterprise's business metadata dictionary, perform cross-source mapping cleaning processing on the enterprise's original data to be cleaned to obtain a standardized business dataset of the original data to be cleaned. S2. Perform high-order feature extraction on the standardized business dataset to obtain the initial business feature set of the standardized business dataset; S3. Based on the dynamic weighting mechanism obtained from the statistical analysis of the enterprise's historical business cycle data, the initial business characteristic set is dynamically weighted and filtered to obtain the optimal set of business characteristics of the initial business characteristic set; S4. Based on DuPont analysis logic, the optimal set of business characteristics is refined to obtain a business analysis and processing benchmark that is adapted to the optimal set of business characteristics. S5. Based on the aforementioned business analysis and processing benchmark, perform diagnostic deduction on the standardized business dataset to obtain a business analysis report of the standardized business dataset.

[0006] In a preferred embodiment, the process of performing cross-source mapping on the enterprise's raw data to be cleaned based on the enterprise's business metadata dictionary to obtain a standardized business dataset of the raw data to be cleaned includes: Semantic decomposition is performed on the business metadata dictionary to obtain the caliber descriptors of each business indicator in the business metadata dictionary; Based on the caliber descriptor, semantic representation encoding is performed on each source field in the original data to be cleaned to obtain the semantic representation vector of each source field; The semantic representation vector is subjected to a cosine spacing metric to obtain a semantic matching degree array for each source field; Based on the semantic matching degree array, the auxiliary caliber fields of each source field are normalized and aggregated to obtain the operational dataset of the original data to be cleaned; Based on the company's historical data from the same business cycle, the operational dataset is cleaned to obtain a standardized operational dataset of the original data to be cleaned.

[0007] In a preferred embodiment, the step of extracting high-order features from the standardized business dataset to obtain an initial business feature set for the standardized business dataset includes: The financial dimension items in the standardized operating dataset are decomposed into time-series trends to obtain the financial robustness derivative characteristics of the financial dimension items; The operational efficiency composite feature of the business process dimension data in the standardized business dataset is obtained by performing process efficiency analysis on the business process dimension data. The customer dimension data in the standardized business dataset is hierarchically valued and aggregated to obtain the customer asset composite characteristics of the customer dimension data. The initial set of operating features for the standardized operating dataset is obtained by performing feature interaction derivation on the financial stability derivative features, the operational efficiency composite features, and the customer assetization composite features.

[0008] In a preferred embodiment, the dynamic weighting mechanism based on the enterprise's historical business cycle data is used to dynamically weight and filter the initial business feature set to obtain an optimized set of business features from the initial business feature set, including: Using the operating quarter as the granularity, seasonal components are filtered out from the historical business cycle data of the segmented enterprises to obtain the de-seasonalized sub-cycle slices of the historical business cycle data. Based on the deseasonalized sub-cycle slices, the initial operating feature set and the preset core operating indicators are subjected to a sliding contemporaneous correlation trend measurement to obtain the periodic correlation sequence of each feature in the initial operating feature set; The time sensitivity value of each feature is obtained by performing range fluctuation time quantification on the phased correlation sequence. Based on the correlation between the time sensitivity value and each feature in the current period, the features are adaptively fused and weighted to obtain the dynamic weight configuration of each feature. Based on the dynamic weight configuration, the cumulative contribution rate of the initial business feature set is extracted to obtain the optimal set of business features of the initial business feature set.

[0009] In a preferred embodiment, the step of performing range fluctuation time-sensitivity quantification on the phased correlation sequence to obtain the time sensitivity value of each feature includes: The range fluctuation measure is performed on the phased correlation sequence to obtain the time-series fluctuation measure value of each feature; Based on the time-series fluctuation metric, the correlation stability of each feature is segmented and thresholded to obtain the stability rating result of each feature. The stability rating results are converted to time sensitivity to obtain the time sensitivity value of each feature.

[0010] In a preferred embodiment, the step of adaptively fusing and weighting each feature in the initial business feature set based on the time sensitivity value and the current correlation to obtain the dynamic weight configuration of each business feature includes: The causal transmission strength of the initial set of operating characteristics is calibrated to obtain the causal transmission coefficients between the operating characteristics. The current correlation degree is standardized and mapped to obtain the normalized current correlation degree of each feature; The stability factor of each feature is obtained by reciprocal normalization of the time sensitivity value. Based on the normalized current correlation, the stability factor, and the causal transmission coefficient, the comprehensive screening goodness of each feature in the initial operating feature set is calculated, wherein the formula for calculating the comprehensive screening goodness is: ; In the formula, Indicates the first The overall screening performance of each feature Indicates the first Normalized current correlation of each feature The function is a non-linear activation function. Indicates the first Stability factor of each characteristic Indicates the first Stability factor of each characteristic Indicates the first The first feature is related to the first The causal transmission coefficient of each characteristic This represents the preset signal amplification steepness factor. This represents the preset causal attenuation penalty coefficient. This indicates the preset time compromise factor; Based on the comprehensive screening merit, the initial set of business features is assigned merit values ​​to obtain the dynamic weight configuration of each business feature.

[0011] In a preferred embodiment, the step of refining the operational feature optimization set based on DuPont analysis logic to obtain an operational analysis processing benchmark adapted to the operational feature optimization set includes: The DuPont identity decomposition of the optimized set of business characteristics yields a profit-layer feature subset, an operational-layer feature subset, and a leverage-layer feature subset of the optimized set of business characteristics. A profit composition decomposition analysis is performed on the profit layer feature subset to obtain the profit quality benchmark parameters of the profit layer feature subset; Efficiency bottlenecks are traced in the subset of operational layer features to obtain a reference for locating the efficiency bottlenecks of the subset of operational layer features. Leverage matching verification is performed on the feature subset of the leverage layer to obtain the leverage risk matching benchmark of the feature subset of the leverage layer; The profitability quality benchmark parameters, the efficiency bottleneck positioning reference, and the leverage risk matching benchmark are calibrated in a coordinated manner to obtain an operational analysis and processing benchmark that is adapted to the optimal set of operational characteristics.

[0012] In a preferred embodiment, the step of tracing efficiency bottlenecks in the operational layer feature subset to obtain a reference for locating efficiency bottlenecks in the operational layer feature subset includes: The asset turnover-related features in the aforementioned operational layer feature subset are classified and grouped according to the procurement, production, and sales stages to obtain the turnover feature groups for each stage of the asset turnover-related features. By working backwards from the turnover days of the turnover characteristic groups of each link, the turnover efficiency metric of each link is obtained. The turnover efficiency metrics of each link are compared in a chain along the business chain from procurement to payment, production to warehousing, and sales to collection to obtain the bottleneck link identification results of each link. The bottleneck with the largest turnover days in the bottleneck identification results is extracted for attribution, and the efficiency bottleneck location reference of the operation layer feature subset is obtained.

[0013] In a preferred embodiment, the step of performing diagnostic deduction on the standardized business dataset based on the business analysis processing benchmark to obtain a business analysis report of the standardized business dataset includes: Based on the aforementioned business analysis and processing benchmark, the standardized business dataset is subjected to item-by-item difference quantification to obtain the deviation measure value of each feature in the standardized business dataset; The deviation measures of each feature are concatenated along the profit dimension, operation dimension, and leverage dimension to obtain the deviation vector of the standardized business dataset. By performing positive and negative force cancellation deduction on the features that exceed the tolerance boundary in the deviation vector, the operational status diagnosis conclusion of the standardized operational dataset is obtained; The operational status diagnosis conclusions are structured into a thesaurus to obtain an operational analysis report of the standardized operational dataset.

[0014] In a preferred embodiment, the step of performing positive and negative force cancellation deduction on the features exceeding the tolerance boundary in the deviation vector to obtain the operational status diagnosis conclusion of the standardized operational dataset includes: By labeling the polarity of each feature in the deviation vector, the deviation direction mapping of the deviation vector is obtained; The positive deviations of the profit dimension, operation dimension, and leverage dimension in the deviation pointing map are summed to obtain the positive driving force of each dimension; The negative deviations of the profit dimension, operation dimension, and leverage dimension in the deviation pointing map are summed to obtain the negative drag force of each dimension. The positive driving forces and negative drags of each dimension are net offset to obtain the business situation diagnosis conclusion of the standardized business dataset.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention relies on the enterprise business metadata dictionary to complete cross-source mapping cleaning and data standardization processing, accurately realizes semantic matching of multi-source business data and standardized aggregation of synonym fields, forming a unified and standardized business dataset, comprehensively ensuring the consistency, integrity and standardization of business data, and laying a high-quality data foundation for subsequent business analysis.

[0016] 2. This invention constructs a dynamic weighting mechanism based on the enterprise's historical business cycle data to complete the optimal selection of business characteristics. It combines DuPont analysis logic to extract exclusive business analysis benchmarks and achieves accurate diagnosis of business situation through multi-dimensional deviation measurement and positive and negative force offsetting deduction. This significantly improves the adaptability of business characteristic screening and the accuracy of analysis conclusions, providing comprehensive, reliable and practical support for enterprise business decision-making. Attached Figure Description

[0017] Figure 1A flowchart illustrating a machine learning-based enterprise management data analysis method according to an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a machine learning-based enterprise business data analysis method. The execution entity of this machine learning-based enterprise business data analysis method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application embodiment: a server, a terminal, etc. In other words, the machine learning-based enterprise business data analysis method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a machine learning-based enterprise management data analysis method according to an embodiment of the present invention. In this embodiment, the machine learning-based enterprise management data analysis method includes: S1. Based on the enterprise's business metadata dictionary, perform cross-source mapping cleaning processing on the enterprise's original data to be cleaned to obtain a standardized business dataset of the original data to be cleaned. In this embodiment of the invention, the process of performing cross-source mapping on the raw data to be cleaned based on the enterprise's business metadata dictionary to obtain a standardized business dataset of the raw data to be cleaned includes: Semantic decomposition is performed on the business metadata dictionary to obtain the caliber descriptors of each business indicator in the business metadata dictionary; Based on the caliber descriptor, semantic representation encoding is performed on each source field in the original data to be cleaned to obtain the semantic representation vector of each source field; The semantic representation vector is subjected to a cosine spacing metric to obtain a semantic matching degree array for each source field; Based on the semantic matching degree array, the auxiliary caliber fields of each source field are normalized and aggregated to obtain the operational dataset of the original data to be cleaned; Based on the company's historical data from the same business cycle, the operational dataset is cleaned to obtain a standardized operational dataset of the original data to be cleaned.

[0021] In the application environment of enterprise operation data analysis methods based on machine learning, the business metadata dictionary that has been prepared and compiled in advance by the enterprise is retrieved. For the core business information such as various business rules, business indicator definitions, and business statistical scope recorded in the business metadata dictionary, a fine-grained analysis operation is performed line by line and category by category to decompose the semantic level, break down the business meaning, and strip away the business attributes. The specific descriptor corresponding to each business indicator in the business metadata dictionary is accurately separated and sorted out.

[0022] Based on the fixed semantic content carried by the business indicator text interpretation, statistical scope boundary, business application scenario, and data statistical dimension of the descriptor obtained after decomposition, the business text information, field attribute information, and business attribution information contained in each source field of the original data to be cleaned are extracted one by one. According to the association logic corresponding to the business semantics, the semantic feature extraction, business meaning summarization, and semantic content uniform regularization representation encoding work is carried out on each source field. After the encoding is completed, an independent semantic representation vector corresponding to each source field is formed.

[0023] All generated semantic representation vectors are incorporated into a unified semantic comparison environment. The degree of business semantic fit between any two semantic representation vectors is compared one by one at the distance level, the degree of difference is judged, and the degree of association is measured. The pairwise matching measurement of the semantic representation vectors corresponding to all source fields is completed in sequence. The semantic matching results obtained from all the measurements are arranged and integrated in an orderly manner according to the field correspondence. After being shaped, a semantic matching degree array that fully covers all source fields is obtained.

[0024] Based on the semantic matching strength of each source field presented by the semantic matching degree array, all auxiliary fields associated with each source field in the original data to be cleaned are accurately located and filtered. Following the unified business data specifications, unified standards of caliber, and business data statistical rules formulated by the enterprise, the selected auxiliary fields are subjected to standardized aggregation and sorting operations such as business meaning alignment, merging of duplicate fields, classification of similar fields, and integration of cross-caliber fields. After the aggregation and sorting are completed, the business dataset corresponding to the original data to be cleaned is constructed in a regular manner.

[0025] Retrieve historical data from the enterprise database that are of the same business type and scope as the current analysis period, and use the normal data range, compliant data format, standard business values, and conventional data logic of the historical data of the same business period as a reference benchmark. Then, compare each data item in the business dataset with the content, field format specifications, business value size, and data relationship logic. Conduct comprehensive data screening and standardization processing in sequence, including abnormal data screening, missing data completion, redundant data deletion, format error correction, and business logic conflict correction. After processing, unify and solidify the data caliber and data format, and finally generate a standardized business dataset corresponding to the original data to be cleaned.

[0026] The beneficial effect is that this method, through semantic decomposition, semantic matching and aggregation, and standardized data cleaning, can quickly achieve the standardization and data regularization of multi-source business data of enterprises, providing accurate, reliable and standardized data support for subsequent business analysis.

[0027] S2. Perform high-order feature extraction on the standardized business dataset to obtain the initial business feature set of the standardized business dataset; In this embodiment of the invention, the step of extracting high-order features from the standardized business dataset to obtain the initial business feature set of the standardized business dataset includes: The financial dimension items in the standardized operating dataset are decomposed into time-series trends to obtain the financial robustness derivative characteristics of the financial dimension items; The operational efficiency composite feature of the business process dimension data in the standardized business dataset is obtained by performing process efficiency analysis on the business process dimension data. The customer dimension data in the standardized business dataset is hierarchically valued and aggregated to obtain the customer asset composite characteristics of the customer dimension data. The initial set of operating features for the standardized operating dataset is obtained by performing feature interaction derivation on the financial stability derivative features, the operational efficiency composite features, and the customer assetization composite features.

[0028] For all financial dimension items in the standardized operating dataset that have undergone data cleaning, standardization, and format normalization, the periodic changes, long-term development trends, and phased fluctuation patterns of the corresponding data for each financial dimension item are checked periodically according to the continuous operating time cycle corresponding to the enterprise's operating statistics and accounting. The time-series trend decomposition process is carried out for all financial dimension items, including full-cycle time dimension change analysis, data change network breakdown, and detailed analysis of financial operating status. The entire decomposition and analysis work is completed based on the actual financial operating status of the enterprise. After the decomposition and analysis is completed, the exclusive characteristics that can intuitively reflect the stable state of the enterprise's financial operations are extracted, and the financial stability derivative characteristics of the financial dimension items are accurately obtained.

[0029] The standardized operational data comprehensively covers the business process dimensions of an enterprise's production and operation, business integration, product services, and contract fulfillment. Following the sequential flow of each operational step in the actual business process, the data is meticulously examined to verify the actual completion status, workflow connection, implementation, and compliance of each step. For each business process step, the data undergoes practical analysis to verify operational effectiveness, process processing speed, and work results. This analysis is conducted in conjunction with the actual business operation flow of the enterprise. After verification, the data is integrated and summarized to accurately obtain the composite operational efficiency characteristics of the business process dimension data.

[0030] The standardized operational data comprehensively records customer dimension data of all enterprise cooperative customers, including cooperation scale, frequency of interaction, consumption contribution, cooperation cycle, and performance reputation. According to the enterprise's established customer operation value evaluation standards, customer cooperation contribution level classification rules, and customer long-term cooperation value definition standards, the data of all customers is processed one by one to carry out hierarchical value collection, which includes precise classification of customer levels, classification and organization of customer cooperation value, and unified collection of customer operation contribution. The collection and sorting work is completed in accordance with the actual standards of enterprise customer operation and management. After the collection and sorting is completed, the relevant characteristics of long-term operation assets of customers are integrated and solidified to accurately obtain the composite characteristics of customer assets in customer dimension data.

[0031] The financial stability features derived from time-series trend analysis, the operational efficiency features derived from process efficiency analysis, and the customer assetization features derived from hierarchical value aggregation are centrally integrated and summarized. This process involves interactive derivation of these three different types of operational features, including feature content matching, complementary feature information, and feature association and expansion. This deeply integrates various independent operational features into comprehensive operational-related features. The entire process revolves around the core needs of overall enterprise operational data analysis to complete the interactive derivation processing. After the interactive derivation processing is completed, all integrated operational feature content is uniformly standardized to accurately obtain the initial operational feature set of the standardized operational dataset.

[0032] The beneficial effect is that by extracting and interactively deriving features from the three dimensions of finance, operations, and customers, core information about a company's operations can be comprehensively explored, and a complete feature system covering all operational dimensions can be constructed, providing comprehensive and accurate feature support for subsequent feature screening and business analysis.

[0033] S3. Based on the dynamic weighting mechanism obtained from the statistical analysis of the enterprise's historical business cycle data, the initial business characteristic set is dynamically weighted and filtered to obtain the optimal set of business characteristics of the initial business characteristic set; In this embodiment of the invention, the dynamic weighting mechanism based on the historical business cycle data of the enterprise is used to dynamically weight and filter the initial business feature set to obtain an optimal set of business features from the initial business feature set, including: Using the operating quarter as the granularity, seasonal components are filtered out from the historical business cycle data of the segmented enterprises to obtain the de-seasonalized sub-cycle slices of the historical business cycle data. Based on the deseasonalized sub-cycle slices, the initial operating feature set and the preset core operating indicators are subjected to a sliding contemporaneous correlation trend measurement to obtain the periodic correlation sequence of each feature in the initial operating feature set; The time sensitivity value of each feature is obtained by performing range fluctuation time quantification on the phased correlation sequence. Based on the correlation between the time sensitivity value and each feature in the current period, the features are adaptively fused and weighted to obtain the dynamic weight configuration of each feature. Based on the dynamic weight configuration, the cumulative contribution rate of the initial business feature set is extracted to obtain the optimal set of business features of the initial business feature set.

[0034] The step of performing range fluctuation time-sensitivity quantification on the phased correlation sequence to obtain the time sensitivity value of each feature includes: The range fluctuation measure is performed on the phased correlation sequence to obtain the time-series fluctuation measure value of each feature; Based on the time-series fluctuation metric, the correlation stability of each feature is segmented and thresholded to obtain the stability rating result of each feature. The stability rating results are converted to time sensitivity to obtain the time sensitivity value of each feature.

[0035] The step of adaptively fusing and weighting each feature in the initial business feature set based on the time sensitivity value and the correlation with the current period to obtain the dynamic weight configuration of each business feature includes: The causal transmission strength of the initial set of operating characteristics is calibrated to obtain the causal transmission coefficients between the operating characteristics. The current correlation degree is standardized and mapped to obtain the normalized current correlation degree of each feature; The stability factor of each feature is obtained by reciprocal normalization of the time sensitivity value. Based on the normalized current correlation, the stability factor, and the causal transmission coefficient, the comprehensive screening goodness of each feature in the initial operating feature set is calculated, wherein the formula for calculating the comprehensive screening goodness is: ; In the formula, Indicates the first The overall screening performance of each feature Indicates the first Normalized current correlation of each feature The function is a non-linear activation function. Indicates the first Stability factor of each characteristic Indicates the first Stability factor of each characteristic Indicates the first The first feature is related to the first The causal transmission coefficient of each characteristic This represents the preset signal amplification steepness factor. This represents the preset causal attenuation penalty coefficient. This indicates the preset time compromise factor; Based on the comprehensive screening merit, the initial set of business features is assigned merit values ​​to obtain the dynamic weight configuration of each business feature.

[0036] Using a fixed operating quarter as the sole data segmentation standard, the company's complete historical business cycle data is precisely segmented into individual units for each natural operating quarter. After the segmentation operation, seasonal interference data components that recur in the fixed quarters of each year are completely filtered out and cleaned up, leaving only the valid data content corresponding to the core changes in the company's operations, thus accurately obtaining the de-seasonalized sub-cycle slices of the historical business cycle data.

[0037] Based on the already processed de-seasonalized sub-period slices as a unified data comparison basis, a special processing work is carried out to compare all the operating features contained in the initial operating feature set with the core operating indicators set in advance according to the core needs of enterprise operation analysis. This work includes periodic synchronous comparison, same-period change comparison, and linkage trend verification. The entire process continuously tracks and records the linkage between feature and indicator changes in each operating stage, and accurately obtains the periodic correlation sequence of each feature in the initial operating feature set.

[0038] For each operational characteristic corresponding to a periodic correlation sequence, a comprehensive review is conducted on the maximum and minimum variation gaps of all periodic correlation content within the sequence. The overall fluctuation range of the periodic correlation sequence throughout its entire process is fully analyzed, and a special processing work on the corresponding range fluctuation measurement is carried out to accurately quantify the actual fluctuation of each operational characteristic correlation change and accurately obtain the time-series fluctuation measurement value of each characteristic.

[0039] Using the time-series fluctuation metric obtained by matching one-to-one as the sole evaluation criterion, and according to the different stability levels corresponding to the enterprise's pre-defined segmentation standards, a segmented threshold rating process is carried out for the data correlation stability performance of each business feature, which involves one-to-one comparison and verification, level matching, and tier delineation. This process accurately determines the stability level of the long-term correlation performance of each business feature and accurately obtains the stability rating results of each feature.

[0040] For each operational characteristic that has been verified and generated a stability rating result, combined with the unified conversion standard for the timeliness of characteristic response in enterprise operational data analysis, and in accordance with the fixed correspondence rule between stability level and timeliness sensitivity, a special processing work is carried out to convert the stability rating result into timeliness sensitivity performance. This accurately quantifies the response speed of each operational characteristic to changes in operational data and precisely obtains the timeliness sensitivity value of each characteristic.

[0041] For all interrelated and influential business characteristics within the initial set of business characteristics, the direct driving effect, indirect influence, and strength of business transmission of each business characteristic on other business characteristics are examined one by one. In combination with the actual transmission logic of the enterprise's actual business operations, a special processing work is carried out to calibrate the strength of causal transmission between business characteristics, accurately determine the level of actual influence transmission between each group of business characteristics, and accurately obtain the causal transmission coefficient between each business characteristic.

[0042] For all relevant content related to the current period corresponding to all business characteristics, in accordance with the standardized mapping specifications for relevance established by the enterprise, the relevant content of the current period with different presentation standards and different performance levels is uniformly adjusted and aligned to the same standard presentation range. The standardized mapping special processing work of unified regularization, alignment adaptation and standardized conversion of the relevant content of the current period is completed, and the normalized current relevance of each feature is accurately obtained.

[0043] For the time sensitivity values ​​of each feature that have been converted, according to the fixed correspondence that the higher the time sensitivity of the feature, the weaker the stability performance, a special processing work of reverse adaptation adjustment of time sensitivity values ​​and reciprocal normalization of unified range is carried out. The content after reverse adjustment is uniformly standardized into a stable correspondence representation content with a unified standard, and the stability factor of each feature is accurately obtained.

[0044] The normalized current correlation, stability factor, and causal transmission coefficient, which have all been processed, are used as the three core evaluation criteria. Following the unified integrated evaluation logic of comprehensive screening and judgment of enterprise operating characteristics, the three core evaluation contents are combined, comprehensively analyzed, and integrated as a whole. The overall quality of each operating characteristic in adapting to the enterprise operating data analysis is verified item by item, and the comprehensive screening quality of each characteristic in the initial set of operating characteristics is accurately calculated.

[0045] Based on the ranking of the comprehensive screening merits of each business feature, and following the fixed assignment rule that the higher the comprehensive screening merit, the higher the weight of the corresponding feature, a dedicated weight allocation is matched for each business feature within the initial business feature set. This completes the special processing work of assigning corresponding merit values ​​to all business features, and accurately obtains the dynamic weight allocation of each business feature.

[0046] According to the dynamic weight configuration of each business characteristic that has been verified, the weight contribution ratio of each business characteristic is accumulated in turn. Based on the cumulative contribution standard of the core characteristics required for enterprise business data analysis, only the core high-quality business characteristics that meet the preset standard are extracted, and the inefficient business characteristics whose contribution ratio does not meet the requirements for analysis are removed. The special processing work of extracting the cumulative contribution rate of the initial business characteristic set is completed accurately, and the optimal set of business characteristics of the initial business characteristic set is obtained accurately.

[0047] Using a fixed operating quarter as the sole data segmentation standard, the company's complete historical business cycle data is precisely segmented into individual units for each natural operating quarter. After the segmentation, seasonal interference data components that recur in the fixed quarters of each year are comprehensively filtered out and cleaned up, retaining only the valid data content corresponding to the core changes in the company's operations. This accurately yields the de-seasonalized sub-cycle slices of the historical business cycle data. These slices provide a clean data foundation free from seasonal interference for subsequent trend measurement, ensuring that subsequent analysis is not affected by fluctuations in the fixed quarters.

[0048] Based on the processed de-seasonalized sub-cycle slices as a unified data comparison foundation, a special processing work was carried out to compare all the operating features contained in the initial operating feature set with the core operating indicators set in advance according to the core needs of enterprise operation analysis. This work included periodic synchronous comparison, contemporaneous change comparison, and linkage trend verification. The entire process continuously tracked and recorded the linkage between feature and indicator changes in each operating stage, accurately obtaining the phased correlation sequence of each feature in the initial operating feature set. This sequence fully recorded the correlation changes between each operating feature and the core operating indicators in different business cycles, providing a direct basis for subsequent stability analysis and comprehensive excellence calculation.

[0049] For each operational characteristic, the phased correlation sequence is comprehensively examined to verify the maximum and minimum variation gaps of all periodic correlations within the sequence. The overall fluctuation range of the phased correlation sequence is fully analyzed, and corresponding range fluctuation measurement is carried out. The actual fluctuation of each operational characteristic is accurately quantified, and the time-series fluctuation measurement value of each characteristic is accurately obtained. This measurement value directly reflects the long-term volatility of the correlation between operational characteristics and core indicators. The larger the value, the more unstable the correlation.

[0050] Using the time-series fluctuation metric obtained through one-to-one matching as the sole evaluation criterion, and according to the pre-defined level segmentation standards corresponding to different stability levels, a special processing work is carried out to specifically evaluate the data correlation stability performance of each business feature, including one-to-one benchmarking, level matching, and level delineation. This accurately determines the stability level of the long-term correlation performance of each business feature and precisely obtains the stability rating result of each feature. This result divides the correlation stability of business features into clear level segments, providing a standardized basis for subsequent time sensitivity calculation.

[0051] For each operational characteristic that has been verified and generated a stability rating result, combined with the unified conversion standard for the timeliness of characteristic response in enterprise operational data analysis, and according to the fixed correspondence rule between stability level and timeliness sensitivity, a special processing work is carried out to convert the stability rating result into timeliness sensitivity performance. This accurately quantifies the response speed of each operational characteristic to changes in operational data, and accurately obtains the timeliness sensitivity value of each characteristic. This value directly reflects the response speed of the operational characteristic to changes in enterprise operations, with lower stability ratings corresponding to higher timeliness sensitivity values.

[0052] For all interrelated and influential business characteristics within the initial set of business characteristics, each business characteristic is examined to verify its direct and indirect impact on other business characteristics, as well as the strength of its business transmission. A special process is then conducted to calibrate the causal transmission strength of the mutual influence between business characteristics, based on the actual transmission logic of the enterprise's business operations. This accurately determines the level of actual influence transmission between each group of business characteristics, precisely obtaining the causal transmission coefficient between each business characteristic. This coefficient directly reflects the strength of the causal influence of other business characteristics on the current business characteristic; a larger value indicates a deeper degree of influence.

[0053] For all relevant content related to the current period corresponding to all business characteristics, the relevant content with different presentation standards and different performance levels is uniformly adjusted and aligned to the same standard presentation range according to the standardized mapping specifications for the correlation degree uniformly formulated by the enterprise. This completes the standardized mapping special processing work of uniformly organizing, aligning and adapting, and standardizing the relevant content of the current period, and accurately obtains the normalized current correlation degree of each feature. This correlation degree is obtained by measuring the sliding contemporaneous correlation trend between each feature of the initial business feature set and the preset core business indicators based on de-seasonal sub-period slices. It is one of the core parameters reflecting the current correlation performance of features in the subsequent comprehensive screening goodness calculation.

[0054] For the time sensitivity values ​​of each feature that have already been converted, a special processing work of reverse adaptation adjustment and reciprocal normalization of time sensitivity values ​​is carried out according to the fixed correspondence that the higher the time sensitivity of a feature, the weaker its stability performance. The content after reverse adjustment is uniformly standardized into a stable correspondence representation content with a unified standard, and the stability factor of each feature is accurately obtained. This factor is obtained by time sensitivity conversion processing of the stability rating results of each feature. The higher the value, the stronger the correlation stability of the business feature. It is one of the core parameters reflecting the long-term stability of the feature in the subsequent comprehensive screening excellence calculation.

[0055] The normalized current correlation, stability factor, and causal transmission coefficient, all of which have been fully processed, are used as the three core evaluation criteria. These are combined with the company's pre-set signal amplification steepness factor, causal attenuation penalty coefficient, and time compromise factor. Following a unified and integrated evaluation logic based on the comprehensive screening and judgment of the company's operating characteristics, the three core evaluation contents and preset factors are comprehensively combined, analyzed, and integrated as a whole. The overall merits and demerits of each operating characteristic in relation to the company's operating data analysis are verified item by item. The comprehensive screening merits of each characteristic in the initial set of operating characteristics are accurately calculated. The preset time compromise factor is used to balance the weighting of current correlation performance and long-term stability performance of a characteristic, and the preset signal amplification steepness factor is used to amplify the differences between the normalized current correlation of different characteristics to enhance discriminative power. The preset causal decay penalty coefficient is used to amplify the negative impact of insufficient stability of other features on the current feature. The higher the overall screening quality, the higher the adaptability and value of the business feature to the analysis of business data. The higher the normalized current correlation and the higher the stability factor, the higher the corresponding overall screening quality. The stronger the causal transmission influence of other features on the current feature and the worse the stability of other features, the lower the overall screening quality of the current feature. The larger the preset time compromise factor, the higher the weight of the influence of the current correlation performance of the feature on the overall screening quality. The larger the preset signal amplification steepness factor, the higher the degree to which the difference between the normalized current correlation of features is amplified. The larger the preset causal decay penalty coefficient, the higher the degree to which the negative impact of insufficient stability of other features on the overall screening quality of the current feature is amplified.

[0056] Based on the ranking of the comprehensive screening merit of each business feature, and following the fixed assignment rule that the higher the comprehensive screening merit, the higher the weight of the corresponding feature, a dedicated weight configuration quota is matched for each business feature within the initial business feature set. This completes the special processing work of assigning targeted merit values ​​to all business features, accurately obtaining the dynamic weight configuration of each business feature. This configuration directly transforms the comprehensive screening merit of the business features into weight parameters that can be directly used in subsequent data analysis, ensuring that high-quality features play a leading role in the analysis process.

[0057] Following the established dynamic weighting of each operational feature, the weight contribution percentage of each operational feature is accumulated sequentially. Based on the cumulative contribution standards for the core features required for enterprise operational data analysis, only high-quality core operational features that meet the preset standards are selected, while inefficient operational features whose contribution percentage does not meet the analysis requirements are removed. This precise process of extracting the cumulative contribution rate of the initial operational feature set results in a precise optimized set of operational features. This optimized set eliminates inefficient features with poor stability, low correlation, and susceptibility to negative impacts from other features, while retaining the core operational features with the strongest adaptability. This provides a precise and reliable feature foundation for subsequent enterprise operational data analysis.

[0058] The beneficial effect is that this method, by integrating time-series correlation, stability and causal transmission into a comprehensive screening quality calculation and dynamic weighted screening mechanism, can accurately extract core features that fit the business status of enterprises, providing high-value and high-reliability feature support for subsequent business analysis.

[0059] S4. Based on DuPont analysis logic, the optimal set of business characteristics is refined to obtain a business analysis and processing benchmark that is adapted to the optimal set of business characteristics. In this embodiment of the invention, the step of refining the operational feature optimization set based on DuPont analysis logic to obtain an operational analysis processing benchmark adapted to the operational feature optimization set includes: The DuPont identity decomposition of the optimized set of business characteristics yields a profit-layer feature subset, an operational-layer feature subset, and a leverage-layer feature subset of the optimized set of business characteristics. A profit composition decomposition analysis is performed on the profit layer feature subset to obtain the profit quality benchmark parameters of the profit layer feature subset; Efficiency bottlenecks are traced in the subset of operational layer features to obtain a reference for locating the efficiency bottlenecks of the subset of operational layer features. Leverage matching verification is performed on the feature subset of the leverage layer to obtain the leverage risk matching benchmark of the feature subset of the leverage layer; The profitability quality benchmark parameters, the efficiency bottleneck positioning reference, and the leverage risk matching benchmark are calibrated in a coordinated manner to obtain an operational analysis and processing benchmark that is adapted to the optimal set of operational characteristics.

[0060] The step of tracing efficiency bottlenecks in the operational layer feature subset to obtain a reference for locating efficiency bottlenecks in the operational layer feature subset includes: The asset turnover-related features in the aforementioned operational layer feature subset are classified and grouped according to the procurement, production, and sales stages to obtain the turnover feature groups for each stage of the asset turnover-related features. By working backwards from the turnover days of the turnover characteristic groups of each link, the turnover efficiency metric of each link is obtained. The turnover efficiency metrics of each link are compared in a chain along the business chain from procurement to payment, production to warehousing, and sales to collection to obtain the bottleneck link identification results of each link. The bottleneck with the largest turnover days in the bottleneck identification results is extracted for attribution, and the efficiency bottleneck location reference of the operation layer feature subset is obtained.

[0061] Using a fixed operating quarter as the sole data segmentation standard, the company's complete historical business cycle data is precisely segmented into individual units for each natural operating quarter. After the segmentation operation, seasonal interference data components that recur in the fixed quarters of each year are completely filtered out and cleaned up, leaving only the valid data content corresponding to the core changes in the company's operations, thus accurately obtaining the de-seasonalized sub-cycle slices of the historical business cycle data.

[0062] Based on the already processed de-seasonalized sub-period slices as a unified data comparison basis, a special processing work is carried out to compare all the operating features contained in the initial operating feature set with the core operating indicators set in advance according to the core needs of enterprise operation analysis. This work includes periodic synchronous comparison, same-period change comparison, and linkage trend verification. The entire process continuously tracks and records the linkage between feature and indicator changes in each operating stage, and accurately obtains the periodic correlation sequence of each feature in the initial operating feature set.

[0063] For each operational characteristic corresponding to a periodic correlation sequence, a comprehensive review is conducted on the maximum and minimum variation gaps of all periodic correlation content within the sequence. The overall fluctuation range of the periodic correlation sequence throughout its entire process is fully analyzed, and a special processing work on the corresponding range fluctuation measurement is carried out to accurately quantify the actual fluctuation of each operational characteristic correlation change and accurately obtain the time-series fluctuation measurement value of each characteristic.

[0064] Using the time-series fluctuation metric obtained by matching one-to-one as the sole evaluation criterion, and according to the different stability levels corresponding to the enterprise's pre-defined segmentation standards, a segmented threshold rating process is carried out for the data correlation stability performance of each business feature, which involves one-to-one comparison and verification, level matching, and tier delineation. This process accurately determines the stability level of the long-term correlation performance of each business feature and accurately obtains the stability rating results of each feature.

[0065] For each operational characteristic that has been verified and generated a stability rating result, combined with the unified conversion standard for the timeliness of characteristic response in enterprise operational data analysis, and in accordance with the fixed correspondence rule between stability level and timeliness sensitivity, a special processing work is carried out to convert the stability rating result into timeliness sensitivity performance. This accurately quantifies the response speed of each operational characteristic to changes in operational data and precisely obtains the timeliness sensitivity value of each characteristic.

[0066] For all relevant content related to the current period corresponding to all business characteristics, in accordance with the standardized mapping specifications for relevance established by the enterprise, the relevant content of the current period with different presentation standards and different performance levels is uniformly adjusted and aligned to the same standard presentation range. The standardized mapping special processing work of unified regularization, alignment adaptation and standardized conversion of the relevant content of the current period is completed, and the normalized current relevance of each feature is accurately obtained.

[0067] The normalized current correlation, stability factor, and causal transmission coefficient, which have all been processed, are used as the three core evaluation criteria. Following the unified integrated evaluation logic of comprehensive screening and judgment of enterprise operating characteristics, the three core evaluation contents are combined, comprehensively analyzed, and integrated as a whole. The overall quality of each operating characteristic in adapting to the enterprise operating data analysis is verified item by item, and the comprehensive screening quality of each characteristic in the initial set of operating characteristics is accurately calculated.

[0068] According to the dynamic weight configuration of each business characteristic that has been verified, the weight contribution ratio of each business characteristic is accumulated in turn. Based on the cumulative contribution standard of the core characteristics required for enterprise business data analysis, only the core high-quality business characteristics that meet the preset standard are extracted, and the inefficient business characteristics whose contribution ratio does not meet the requirements for analysis are removed. The special processing work of extracting the cumulative contribution rate of the initial business characteristic set is completed accurately, and the optimal set of business characteristics of the initial business characteristic set is obtained accurately.

[0069] The profit layer feature subset, derived from the decomposition of DuPont identities, is comprehensively analyzed. This analysis examines all features within the subset that constitute the core sources of a company's operating profit, the composition of profit revenue and expenditure, and changes in profit. Each profit-related feature is broken down into its corresponding profit revenue components, profit expense deductions, and profit retention / carryover. A detailed profit composition breakdown analysis is conducted, comparing the profit structure with the company's actual operating profit accounting, verifying the company's fundamental profitability. This process accurately identifies a unified reference standard that directly represents the company's true profit level and the rationality of its profit structure, thus precisely obtaining the profit quality benchmark parameters for the aforementioned profit layer feature subset.

[0070] For each of the procurement, production, and sales stages, corresponding to specific turnover characteristic groups, the actual execution time, asset turnover completion cycle, and operation completion time of all asset turnover operations within each turnover characteristic group are verified one by one. Combined with the actual operation completion process of the enterprise's asset turnover operation, and according to the complete operation cycle from asset use to the completion of the turnover loop, a special processing work is carried out to reverse calculate the actual asset turnover time of each stage, reverse calculate the turnover completion cycle, and accurately calculate the turnover days to precisely quantify the actual speed of asset turnover operation in each business operation stage, and accurately obtain the turnover efficiency metric value of each stage.

[0071] Based on the identified bottlenecks, the core bottleneck business processes with the highest turnover days, the most significant operational constraints, and the most prominent flow delays are precisely identified. A comprehensive analysis of all operational influencing factors, reasons for operational constraints, process bottlenecks, and weak management points corresponding to these core bottleneck processes is conducted. Each core root cause of low turnover efficiency and operational delays in these processes is extracted. A dedicated attribution extraction process is implemented, involving in-depth analysis of bottleneck problems, extraction of core causes, and precise aggregation of constraining factors. This provides a precise positioning basis for subsequent optimization and adjustment of enterprise operational efficiency, accurately obtaining the efficiency bottleneck positioning reference for the aforementioned operational layer feature subset.

[0072] By retrieving the previously analyzed and processed profit quality benchmark parameters, efficiency bottleneck positioning references, and leverage risk matching benchmarks, and combining them with the overall analysis and adaptation requirements of a machine learning-based enterprise operation data analysis method, a special linkage calibration process is carried out on the three core benchmark contents. This process involves mutual adaptation verification, data linkage calibration, and standard unification and standardization. The degree of fit between the three benchmarks is adjusted simultaneously to eliminate adaptation deviations and connection differences between benchmarks of different operation dimensions. This ensures that the three benchmark contents match each other and synergistically adapt to the overall enterprise operation data analysis needs. The process accurately standardizes and forms a unified analysis and judgment standard that adapts to the actual operation of the enterprise, and accurately obtains the operation analysis and processing benchmark that adapts to the optimal set of operation characteristics.

[0073] The beneficial effects of this method are that it can quickly build a unified and standardized business analysis benchmark through DuPont decomposition, three-dimensional feature analysis and standard linkage calibration, accurately locate operational bottlenecks and clarify the judgment criteria for each dimension, and provide reliable support for subsequent business diagnosis.

[0074] S5. Based on the aforementioned business analysis and processing benchmark, perform diagnostic deduction on the standardized business dataset to obtain a business analysis report of the standardized business dataset.

[0075] In this embodiment of the invention, the step of performing diagnostic deduction on the standardized business dataset based on the business analysis processing benchmark to obtain a business analysis report of the standardized business dataset includes: Based on the aforementioned business analysis and processing benchmark, the standardized business dataset is subjected to item-by-item difference quantification to obtain the deviation measure value of each feature in the standardized business dataset; The deviation measures of each feature are concatenated along the profit dimension, operation dimension, and leverage dimension to obtain the deviation vector of the standardized business dataset. By performing positive and negative force cancellation deduction on the features that exceed the tolerance boundary in the deviation vector, the operational status diagnosis conclusion of the standardized operational dataset is obtained; The operational status diagnosis conclusions are structured into a thesaurus to obtain an operational analysis report of the standardized operational dataset.

[0076] The process of performing positive and negative force cancellation deduction on the features exceeding the tolerance boundary in the deviation vector yields operational status diagnostic conclusions for the standardized operational dataset, including: By labeling the polarity of each feature in the deviation vector, the deviation direction mapping of the deviation vector is obtained; The positive deviations of the profit dimension, operation dimension, and leverage dimension in the deviation pointing map are summed to obtain the positive driving force of each dimension; The negative deviations of the profit dimension, operation dimension, and leverage dimension in the deviation pointing map are summed to obtain the negative drag force of each dimension. The positive driving forces and negative drags of each dimension are net offset to obtain the business situation diagnosis conclusion of the standardized business dataset.

[0077] Using the operational analysis and processing benchmark completed in the early stage of linkage calibration as the sole benchmark, the complete and retained standardized operational dataset is retrieved. The actual data content of each operational feature contained in the standardized operational dataset is individually compared with the standard specification content corresponding to the operational analysis and processing benchmark. This is a special process of quantifying the differences item by item, accurately verifying the size and degree of deviation between the actual performance of each operational feature and the benchmark requirements, and solidifying the quantitative results of the actual deviation corresponding to each operational feature. The deviation measurement value of each feature in the standardized operational dataset is then accurately obtained.

[0078] The deviation measures of all features that have been quantified are then processed according to the three fixed business categories of profit, operation and leverage. The deviation measures corresponding to each single feature are categorized and organized according to the corresponding dimension, the content of the same dimension is collected, and the content of cross-dimensional content is linked in an orderly manner. All deviation measures belonging to the same business dimension are connected and integrated in a fixed order, and a unified summary is formed to form a complete data combination that covers all business dimensions and includes all feature deviations, so as to accurately obtain the deviation vector of the standardized business dataset.

[0079] For each operational feature within the standardized and integrated deviation vector, according to the pre-defined operational deviation direction judgment rules set by the enterprise, a special processing work is carried out on the symbol polarity labeling of each feature's deviation status, distinguishing between positive and negative deviation statuses, and defining the deviation direction attribute. This clearly identifies whether each operational feature is higher or lower than the operational analysis benchmark, unifies and standardizes the deviation polarity labeling correspondence of all features, and accurately obtains the deviation direction mapping of the deviation vector.

[0080] Based on the polarity correspondence of the internal annotations of the deviation direction mapping after the model is formed, all feature deviation data marked as positive deviation within the jurisdiction of the profit dimension, operation dimension and leverage dimension are individually selected and extracted. The quantitative values ​​corresponding to all positive deviations under each operation dimension are summed, summarized and merged into a special processing work to calculate the total positive operation driving force of each operation dimension.

[0081] Based on the polarity correspondence of the internal annotations of the deviation direction mapping after the model is formed, all feature deviation data marked as negative deviation within the jurisdiction of the profit dimension, operation dimension and leverage dimension are individually screened and extracted. The quantitative values ​​corresponding to all negative deviations under each operation dimension are summed, summarized and merged into a special processing work to calculate the total negative drag effect of each operation dimension as a whole, and accurately obtain the negative drag force of each dimension.

[0082] For the positive driving forces and negative drags calculated separately for the profitability, operation, and leverage dimensions, a special net offset determination process is carried out according to the fixed judgment rules for net accounting of the enterprise's operating status. The positive driving force value and negative drag value corresponding to the same operating dimension are mutually offset and offset, net difference accounting is performed, and operating performance is comprehensively judged. Combining the net offset results of the three operating dimensions and the comprehensive offset performance of the overall operating dimensions, the actual development trend, operating advantages and disadvantages, and development status of the enterprise's overall operation are comprehensively analyzed, and the operating status diagnosis conclusion of the standardized operating dataset is accurately obtained.

[0083] Based on the final business situation diagnosis conclusions generated by the analysis, and following the fixed structured compilation format of enterprise business data analysis reports, the standardization of professional business terminology usage, and the logical arrangement of report content, a special processing work is carried out on the various core business diagnostic information, dimensional analysis content, deviations from actual situation, and judgments of business strengths and weaknesses contained in the business situation diagnosis conclusions. This work involves professional terminology replacement, structured paragraph splitting, and logically ordered structured terminology arrangement. The scattered diagnostic judgment content is organized into a clear, professionally expressed, complete, readable, and standardized formal text content, accurately obtaining the business analysis report of the standardized business dataset.

[0084] The beneficial effects of this method are that, through differential quantification, dimensional linkage, positive and negative force offsetting deduction, and structured thesaurus arrangement, it can quickly complete the deviation analysis of business data and diagnosis of business situation, and efficiently output standardized and complete business analysis reports, providing intuitive and accurate basis for enterprise business decision-making.

[0085] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0086] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A machine learning-based method for analyzing enterprise operational data, characterized in that, The method includes: S1. Based on the enterprise's business metadata dictionary, perform cross-source mapping cleaning processing on the enterprise's original data to be cleaned to obtain a standardized business dataset of the original data to be cleaned. S2. Perform high-order feature extraction on the standardized business dataset to obtain the initial business feature set of the standardized business dataset; S3. Based on the dynamic weighting mechanism obtained from the statistical analysis of the enterprise's historical business cycle data, the initial business characteristic set is dynamically weighted and filtered to obtain the optimal set of business characteristics of the initial business characteristic set; S4. Based on DuPont analysis logic, the optimal set of business characteristics is refined to obtain a business analysis and processing benchmark that is adapted to the optimal set of business characteristics. S5. Based on the aforementioned business analysis and processing benchmark, perform diagnostic deduction on the standardized business dataset to obtain a business analysis report of the standardized business dataset.

2. The enterprise management data analysis method based on machine learning as described in claim 1, characterized in that, The enterprise-based business metadata dictionary performs cross-source mapping processing on the raw data to be cleaned, resulting in a standardized operational dataset of the raw data to be cleaned, including: Semantic decomposition is performed on the business metadata dictionary to obtain the caliber descriptors of each business indicator in the business metadata dictionary; Based on the caliber descriptor, semantic representation encoding is performed on each source field in the original data to be cleaned to obtain the semantic representation vector of each source field; The semantic representation vector is subjected to a cosine spacing metric to obtain a semantic matching degree array for each source field; Based on the semantic matching degree array, the auxiliary caliber fields of each source field are normalized and aggregated to obtain the operational dataset of the original data to be cleaned; Based on the company's historical data from the same business cycle, the operational dataset is cleaned to obtain a standardized operational dataset of the original data to be cleaned.

3. The enterprise management data analysis method based on machine learning as described in claim 1, characterized in that, The step of extracting high-order features from the standardized business dataset to obtain the initial business feature set of the standardized business dataset includes: The financial dimension items in the standardized operating dataset are decomposed into time-series trends to obtain the financial robustness derivative characteristics of the financial dimension items; The operational efficiency composite feature of the business process dimension data in the standardized business dataset is obtained by performing process efficiency analysis on the business process dimension data. The customer dimension data in the standardized business dataset is hierarchically valued and aggregated to obtain the customer asset composite characteristics of the customer dimension data. The initial set of operating features for the standardized operating dataset is obtained by performing feature interaction derivation on the financial stability derivative features, the operational efficiency composite features, and the customer assetization composite features.

4. The enterprise management data analysis method based on machine learning as described in claim 1, characterized in that, The dynamic weighting mechanism, based on the enterprise's historical business cycle data, dynamically weights and filters the initial business characteristic set to obtain an optimal set of business characteristics, including: Using the operating quarter as the granularity, seasonal components are filtered out from the historical business cycle data of the segmented enterprises to obtain the de-seasonalized sub-cycle slices of the historical business cycle data. Based on the deseasonalized sub-cycle slices, the initial operating feature set and the preset core operating indicators are subjected to a sliding contemporaneous correlation trend measurement to obtain the periodic correlation sequence of each feature in the initial operating feature set; The time sensitivity value of each feature is obtained by performing range fluctuation time quantification on the phased correlation sequence. Based on the correlation between the time sensitivity value and each feature in the current period, the features are adaptively fused and weighted to obtain the dynamic weight configuration of each feature. Based on the dynamic weight configuration, the cumulative contribution rate of the initial business feature set is extracted to obtain the optimal set of business features of the initial business feature set.

5. The enterprise management data analysis method based on machine learning as described in claim 4, characterized in that, The step of performing range fluctuation time-sensitivity quantification on the phased correlation sequence to obtain the time sensitivity value of each feature includes: The range fluctuation measure is performed on the phased correlation sequence to obtain the time-series fluctuation measure value of each feature; Based on the time-series fluctuation metric, the correlation stability of each feature is segmented and thresholded to obtain the stability rating result of each feature. The stability rating results are converted to time sensitivity to obtain the time sensitivity value of each feature.

6. The enterprise management data analysis method based on machine learning as described in claim 4, characterized in that, The step of adaptively fusing and weighting each feature in the initial business feature set based on the time sensitivity value and the correlation with the current period to obtain the dynamic weight configuration of each business feature includes: The causal transmission strength of the initial set of operating characteristics is calibrated to obtain the causal transmission coefficients between the operating characteristics. The current correlation degree is standardized and mapped to obtain the normalized current correlation degree of each feature; The stability factor of each feature is obtained by reciprocal normalization of the time sensitivity value. Based on the normalized current correlation, the stability factor, and the causal transmission coefficient, the comprehensive screening goodness of each feature in the initial operating feature set is calculated, wherein the formula for calculating the comprehensive screening goodness is: ； In the formula, Indicates the first The overall screening performance of each feature Indicates the first Normalized current correlation of each feature The function is a non-linear activation function. Indicates the first Stability factor of each characteristic Indicates the first Stability factor of each characteristic Indicates the first The first feature is related to the first The causal transmission coefficient of each characteristic This represents the preset signal amplification steepness factor. This represents the preset causal attenuation penalty coefficient. This indicates the preset time compromise factor; Based on the comprehensive screening merit, the initial set of business features is assigned merit values ​​to obtain the dynamic weight configuration of each business feature.

7. The enterprise management data analysis method based on machine learning as described in claim 1, characterized in that, The process of extracting patterns from the optimized set of business characteristics based on DuPont analysis logic to obtain a business analysis processing benchmark adapted to the optimized set of business characteristics includes: The DuPont identity decomposition of the optimized set of business characteristics yields a profit-layer feature subset, an operational-layer feature subset, and a leverage-layer feature subset of the optimized set of business characteristics. A profit composition decomposition analysis is performed on the profit layer feature subset to obtain the profit quality benchmark parameters of the profit layer feature subset; Efficiency bottlenecks are traced in the subset of operational layer features to obtain a reference for locating the efficiency bottlenecks of the subset of operational layer features. Leverage matching verification is performed on the feature subset of the leverage layer to obtain the leverage risk matching benchmark of the feature subset of the leverage layer; The profitability quality benchmark parameters, the efficiency bottleneck positioning reference, and the leverage risk matching benchmark are calibrated in a coordinated manner to obtain an operational analysis and processing benchmark that is adapted to the optimal set of operational characteristics.

8. The enterprise management data analysis method based on machine learning as described in claim 7, characterized in that, The step of tracing efficiency bottlenecks in the operational layer feature subset to obtain a reference for locating efficiency bottlenecks in the operational layer feature subset includes: The asset turnover-related features in the aforementioned operational layer feature subset are classified and grouped according to the procurement, production, and sales stages to obtain the turnover feature groups for each stage of the asset turnover-related features. By working backwards from the turnover days of the turnover characteristic groups of each link, the turnover efficiency metric of each link is obtained. The turnover efficiency metrics of each link are compared in a chain along the business chain from procurement to payment, production to warehousing, and sales to collection to obtain the bottleneck link identification results of each link. The bottleneck with the largest turnover days in the bottleneck identification results is extracted for attribution, and the efficiency bottleneck location reference of the operation layer feature subset is obtained.

9. The enterprise management data analysis method based on machine learning as described in claim 1, characterized in that, The step of performing diagnostic analysis and deduction on the standardized business dataset based on the business analysis processing benchmark to obtain a business analysis report of the standardized business dataset includes: Based on the aforementioned business analysis and processing benchmark, the standardized business dataset is subjected to item-by-item difference quantification to obtain the deviation measure value of each feature in the standardized business dataset; The deviation measures of each feature are concatenated along the profit dimension, operation dimension, and leverage dimension to obtain the deviation vector of the standardized business dataset. By performing positive and negative force cancellation deduction on the features that exceed the tolerance boundary in the deviation vector, the operational status diagnosis conclusion of the standardized operational dataset is obtained; The operational status diagnosis conclusions are structured into a thesaurus to obtain an operational analysis report of the standardized operational dataset.

10. The enterprise management data analysis method based on machine learning as described in claim 9, characterized in that, The process of performing positive and negative force cancellation deduction on the features exceeding the tolerance boundary in the deviation vector yields operational status diagnostic conclusions for the standardized operational dataset, including: By labeling the polarity of each feature in the deviation vector, the deviation direction mapping of the deviation vector is obtained; The positive deviations of the profit dimension, operation dimension, and leverage dimension in the deviation pointing map are summed to obtain the positive driving force of each dimension; The negative deviations of the profit dimension, operation dimension, and leverage dimension in the deviation pointing map are summed to obtain the negative drag force of each dimension. The positive driving forces and negative drags of each dimension are net offset to obtain the business situation diagnosis conclusion of the standardized business dataset.

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