Intelligent enterprise operation data analysis system based on artificial intelligence

Abstract

The invention relates to the technical field of enterprise data processing, and discloses an intelligent analysis system for enterprise operation data based on artificial intelligence. The system comprises an operation data acquisition module, an operation state analysis module, a decision tree generation module, a data compensation module and an execution parameter generation module. The operation data acquisition module acquires enterprise finance, supply chain logistics and market dynamic data in real time, integrates and cleans multi-source heterogeneous data, and generates an operation state data set; the operation state analysis module identifies a deviation degree between the key business index and a preset threshold value, and calculates a multi-dimensional abnormal value by means of a dynamic weighting strategy to obtain an abnormal feature set; a decision-making tree generation module constructs a multi-layer decision-making path according to the multi-layer decision-making path, matches an optimal branch in combination with a historical case library, and generates a visual decision-making tree; the data compensation module analyzes the influence of missing data and automatically supplements an external data source to generate a compensation parameter set; and the execution parameter generation module analyzes node priorities, quantifies resource configuration and time window parameters, and generates a strategy execution parameter packet.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of enterprise data processing, in particular to an enterprise operation data intelligent analysis system based on artificial intelligence. BACKGROUND

[0002] In the current enterprise operation management process, data has become an important element to support business decision-making and development. With the continuous expansion of enterprise business scale and the increasing complexity of business scenarios, the types of data generated in the enterprise operation process are increasingly diverse, covering multiple dimensions such as finance, supply chain, and market, and these data often exhibit multi-source and heterogeneous characteristics, coming from different business systems and having large differences in data formats, which brings great challenges to data integration and utilization.

[0003] Traditional enterprise operation data processing methods mostly rely on manual operation or simple data analysis tools. In the data collection link, only single-type or single-source data can usually be collected, and real-time synchronous collection of financial data flow, supply chain logistics data, and market dynamic data is difficult to achieve, resulting in data acquisition lag and inability to timely reflect the current operation status of the enterprise. Even if some enterprises try to integrate multi-source data, the lack of effective data cleaning mechanisms results in a large amount of redundant, erroneous, or missing information in the collected data, seriously affecting the quality of the data and thus failing to provide a reliable data foundation for subsequent operation analysis.

[0004] In terms of operation status analysis, traditional methods often only analyze individual business indicators separately, without building a multi-dimensional indicator analysis system, making it difficult to comprehensively identify potential problems in the enterprise operation process. Moreover, the judgment of the deviation degree between business indicators and preset thresholds is relatively simple, lacking a dynamic adjustment strategy, and unable to optimize the analysis standards in real time according to changes in enterprise business or fluctuations in market environment, resulting in a disconnect between the analysis results and the actual operation needs of the enterprise, making it difficult to accurately discover abnormal situations in operation.

[0005] When an operation anomaly is discovered, the traditional decision-making process relies on the experience of management personnel, lacking a scientific decision support system. Since there is no mechanism to associate historical disposal case libraries and decision paths, management personnel have difficulty quickly matching the optimal disposal scheme when faced with complex abnormal situations, resulting in low decision-making efficiency and the inability to guarantee the accuracy and reasonableness of the decisions. At the same time, in the decision-making process, the impact of missing data on the completeness of the decision is often overlooked, and external data sources related to the business scenario are not supplemented in time, making the basis for decision-making insufficient and further increasing the risk of decision-making errors.

[0006] During the decision-making and execution phase, traditional methods cannot effectively analyze the priority of execution nodes in the decision-making path, nor can they quantify resource allocation ratios and time window parameters. This leads to unreasonable resource allocation and chaotic time scheduling during the execution process, affecting the effective implementation of decision-making strategies and failing to promptly resolve problems that arise in enterprise operations. Ultimately, this may have an adverse impact on the enterprise's operating efficiency and market competitiveness. Summary of the Invention

[0007] The purpose of this invention is to provide an intelligent analysis system for enterprise operation data based on artificial intelligence, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides an intelligent analysis system for enterprise operation data based on artificial intelligence, the system comprising:

[0009] The operational data acquisition module is used to collect enterprise financial data flow, supply chain logistics data and market dynamic data in real time, integrate multi-source heterogeneous data and perform data cleaning to generate enterprise operational status datasets;

[0010] The operational status analysis module, based on the enterprise operational status dataset, identifies the deviation between key business indicators and preset thresholds, calculates multi-dimensional business status anomalies through a dynamic weighting strategy, and generates an anomaly feature set.

[0011] The decision tree generation module constructs a multi-level decision path based on the abnormal feature set, matches the optimal decision branch with the historical handling case library, and generates a visual decision tree architecture.

[0012] The data compensation module, based on the decision tree architecture and the abnormal feature set, analyzes the impact of missing data on the completeness of decision-making, automatically supplements external data sources related to the business scenario, and generates a data compensation parameter set.

[0013] The execution parameter generation module, based on the decision tree architecture and data compensation parameter set, parses the priority of execution nodes in the decision path, quantifies the resource allocation ratio and time window parameters, and generates a strategy execution parameter package.

[0014] Preferably, the generation of the enterprise operation status dataset specifically involves:

[0015] Configure financial data collection interfaces, supply chain data collection interfaces, and market data collection interfaces to capture real-time transaction volume, inventory turnover rate, and market competition index, respectively.

[0016] Perform timestamp alignment on multi-source heterogeneous data, remove outliers and fill in missing fields;

[0017] The cleaned data is integrated through a data feature fusion engine to generate a dataset of enterprise operational status.

[0018] Preferably, the generation of the abnormal feature set specifically involves:

[0019] Extract cost-benefit ratio, order delivery delay rate, and market share fluctuation values ​​from the enterprise operation status dataset;

[0020] Calculate the dynamic deviation of key business indicators from industry benchmark thresholds;

[0021] A sliding window mechanism is used to analyze the probability of multiple indicators working together to generate an anomaly feature set.

[0022] Preferably, the generated visual decision tree architecture specifically refers to:

[0023] Parse the anomaly type identifier and impact level parameter in the anomaly feature set;

[0024] Match the effectiveness weight of the handling path for the same type of anomaly in the historical case database;

[0025] Construct a multi-branch decision network starting from the abnormal root node to generate a decision tree architecture.

[0026] Preferably, the generated data compensation parameter set specifically includes:

[0027] Identify decision branch nodes in the decision tree architecture that lack data integrity;

[0028] Supplement external correlation factors by accessing macroeconomic indicator databases and industry intelligence databases;

[0029] The improvement in decision confidence by supplementary data is quantified, and a set of data compensation parameters is generated.

[0030] Preferably, the generated strategy execution parameter package specifically comprises:

[0031] Analyze the execution sequence of key decision nodes in the decision tree architecture;

[0032] Quantify human resource allocation coefficients, funding allocation ratios, and execution time window thresholds;

[0033] The associated data compensation parameter set optimizes the execution priority configuration and generates a strategy execution parameter package.

[0034] Preferably, the system further includes: a dynamic correction module, which, based on the strategy execution parameter package and the real-time updated enterprise operation status dataset, monitors environmental variable disturbances during execution, dynamically adjusts the execution node weights and resource allocation paths, and generates a strategy optimization correction set; the process of generating the strategy optimization correction set is as follows:

[0035] Real-time acquisition of market mutation indicators from updated enterprise operational status datasets;

[0036] Detect the degree of conflict between the strategy execution parameter package and the real-time environment parameters;

[0037] The execution weights of decision nodes are recalculated and resource allocation paths are adjusted to generate a strategy optimization correction set.

[0038] Preferably, the system further includes: a resource allocation module, which, based on the strategy optimization and correction set and the execution parameter package, divides the business department execution domain, calculates the cross-departmental collaborative resource scheduling sequence, and generates a dynamic resource allocation scheme; the process of generating the dynamic resource allocation scheme is as follows:

[0039] Parse the cross-departmental collaboration requirement identifiers in the strategy optimization and correction set;

[0040] Calculate the time series of resource scheduling for the production, warehousing, and sales departments;

[0041] Optimize the matching parameters between cash flow and logistics to generate dynamic resource allocation schemes.

[0042] Preferably, the system further includes: an operational indicator generation module, which, based on the dynamic resource allocation scheme and strategy execution parameter package, extracts the change trajectory of key performance indicators and generates an enterprise operational health data table by associating it with the time dimension. The process of generating the enterprise operational health data table is as follows:

[0043] Extract the resource utilization rate curve from the aforementioned dynamic resource allocation scheme;

[0044] The target achievement time point in the associated strategy execution parameter package;

[0045] By integrating the cash flow health index and customer satisfaction trend value, a data table on the health of enterprise operations is generated.

[0046] Preferably, the system further includes: a feedback optimization module, which, based on the enterprise operational health data table, identifies the continuous deviation between key performance indicators and preset target values, extracts the characteristics of the deviation duration and fluctuation range, and generates a strategy execution feedback report;

[0047] Based on the strategy execution feedback report, adjust the threshold for calculating the deviation of key business indicators in the operation status analysis module, and update the weight allocation parameters of the dynamic weighted strategy.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] At the data acquisition level, the system enables real-time collection of enterprise financial data flows, supply chain logistics data, and market dynamic data. This breaks the limitations of traditional data collection methods, eliminating restrictions based on single data types or sources and ensuring that enterprises can obtain comprehensive operational data in a timely manner. Simultaneously, the system has the capability to integrate multi-source heterogeneous data and, through an effective data cleaning process, removes redundant and erroneous information, reduces data gaps, and improves the quality of enterprise operational status datasets. This provides more reliable data support for subsequent operational analysis, helping enterprises to more accurately grasp their own operational situation.

[0050] In the operational status analysis phase, the system constructs a multi-dimensional business indicator analysis system based on high-quality enterprise operational status datasets. It moves beyond the limitations of analyzing individual indicators and comprehensively identifies the deviation between key business indicators and preset thresholds. Furthermore, by using a dynamic weighting strategy to calculate multi-dimensional business status anomalies, the system can adjust weights in real time according to changes in enterprise business and market fluctuations. This makes the calculation of anomalies more closely aligned with the actual operational needs of the enterprise, and the generated anomaly feature set more accurately reflects abnormal situations in enterprise operations. This helps enterprises promptly identify potential problems and prevent them from escalating and causing greater impact on enterprise operations.

[0051] The application of the decision tree generation module changes the traditional decision-making model that relies on human experience. The system constructs multi-level decision paths based on anomaly feature sets, combining historical case libraries with these paths to quickly match the optimal decision branch and generate a visual decision tree architecture. This visual presentation makes the decision paths clearer and easier to understand, allowing managers to intuitively grasp the decision logic. It reduces the subjectivity and uncertainty of human experience-based judgment, improving the efficiency and scientific rigor of decision-making, and enabling companies to quickly formulate appropriate solutions when facing operational anomalies.

[0052] The data compensation module addresses the issue of missing data in the decision-making process. It analyzes the impact of missing data on the completeness of the decision and automatically supplements the data with external data sources related to the business scenario, generating a data compensation parameter set. This function compensates for the deficiency of insufficient data in traditional decision-making processes, providing more comprehensive and richer data support for decision-making, reducing the possibility of decision errors due to missing data, further ensuring the accuracy and completeness of decisions, and making enterprise decision-making more scientific and reliable.

[0053] The execution parameter generation module, based on a decision tree architecture and data compensation parameter set, effectively analyzes the priority of execution nodes in the decision path, clarifies the sequence of each execution step, and avoids confusion during execution. Simultaneously, this module quantifies resource allocation ratios and time window parameters, making resource allocation more rational and ensuring that resources are accurately invested in key execution nodes, while also making time scheduling more planned and actionable. By generating strategy execution parameter packages, it provides a clear execution basis for the implementation of decision-making strategies, ensuring smooth and efficient execution of decisions, timely resolution of operational problems, and helping enterprises improve operational efficiency, enhance market competitiveness, and better cope with market changes and business challenges. Attached Figure Description

[0054] Figure 1 This is a sequence diagram of the AI-based intelligent analysis system for enterprise operation data described in this invention.

[0055] Figure 2 A flowchart for generating a dataset of enterprise operational status;

[0056] Figure 3 To generate a flowchart for a visual decision tree architecture;

[0057] Figure 4 A flowchart for generating the strategy execution parameter package;

[0058] Figure 5 A flowchart for generating a dynamic resource allocation scheme. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Please see Figure 1 This invention provides an intelligent analysis system for enterprise operation data based on artificial intelligence. The system includes multiple collaborative modules for intelligent processing and decision support of enterprise operation data.

[0061] The operational data acquisition module collects real-time enterprise financial data flow, supply chain logistics data, and market dynamic data, integrates multi-source heterogeneous data, performs data cleaning, and generates an enterprise operational status dataset. The operational status analysis module, based on this dataset, identifies the deviation between key business indicators and preset thresholds, calculates multi-dimensional business status anomalies using a dynamic weighting strategy, and generates an anomaly feature set. The decision tree generation module, based on this anomaly feature set, constructs a multi-level decision path, matches the optimal decision branch with a historical case library, and generates a visual decision tree architecture. The data compensation module, based on this decision tree architecture and the anomaly feature set, analyzes the impact of missing data on decision integrity, automatically supplements external data sources related to business scenarios, and generates a data compensation parameter set. The execution parameter generation module, based on this decision tree architecture and the data compensation parameter set, analyzes the priority of execution nodes in the decision path, quantifies resource allocation ratios and time window parameters, and generates a strategy execution parameter package.

[0062] Example 1: See Figure 2 The implementation of the operational data acquisition module involves configuring multiple types of data acquisition interfaces, including financial data acquisition interfaces, supply chain data acquisition interfaces, and market data acquisition interfaces. The financial data acquisition interface is designed to capture real-time transaction data of the enterprise, covering revenue flow, expense details, and cash flow trends. The interface establishes an application programming interface (API) connection with the enterprise's existing financial management system, pulling data streams at fixed time intervals, which can be configured to be on the order of seconds or minutes according to business needs. After preliminary parsing, key fields such as transaction timestamps, transaction amounts, transaction type identifiers, and account balance changes are extracted to form structured financial data packages. The supply chain data acquisition interface is responsible for acquiring inventory turnover rate-related data, which comes from the enterprise's warehouse management system and logistics tracking system. The interface collects dynamic changes in raw material inventory levels, work-in-process inventory status, and finished goods inventory quantity, calculating the inventory turnover cycle indicator. The turnover cycle is derived by recording the time difference between material receipt and receipt times, and combined with inventory quantity to calculate the turnover rate per unit time. The market data acquisition interface focuses on collecting market competition index data, which comes from public market reports, competitor information, and consumer behavior data sources. The interface integrates web crawler technology to automatically capture competitors' pricing information, obtains market share change trends through data aggregation services, and connects to consumer research platforms to extract consumer behavior pattern data.

[0063] The integration and processing of multi-source heterogeneous data requires coordinating different data formats and time frequencies. Structured financial data is presented in database table format, containing numerical fields and timestamp fields; semi-structured supply chain data uses log file format, containing a mixture of text records and numerical content; unstructured market data includes PDF reports, web page content, and social media text. Timestamp alignment operations employ a unified time base conversion technology to convert time information from all data sources to the Coordinated Universal Time (UTC) standard. Time series alignment algorithms process data streams with different collection frequencies, aligning high-frequency financial data with low-frequency market data to a unified time granularity. The algorithm identifies the time index of each data stream and resamples the data to a common time point through interpolation or aggregation methods, forming a time-consistent multidimensional data sequence.

[0064] The data cleaning process comprises two main stages: outlier removal and missing field completion. Outlier identification is based on statistical methods, calculating the numerical distribution characteristics of each data field and setting a threshold based on the standard deviation. Data points exceeding the threshold are considered outliers, and the system automatically marks and excludes them. Missing field completion employs various interpolation techniques. For numerical fields, linear interpolation is used to estimate missing values ​​based on the numerical trends over time. For categorical fields, pattern imputation is used, selecting the most common value for completion. After data cleaning, all data fields maintain integrity and consistency, providing a reliable foundation for subsequent analysis.

[0065] The data feature fusion engine integrates cleaned multi-source data into a unified-format enterprise operational status dataset. The engine applies feature extraction technology to identify key feature dimensions in various data types. Cost structure features in financial data are derived by calculating cost composition ratios; delivery efficiency features in supply chain data are obtained by analyzing order processing time series; and penetration rate features in market data are obtained by assessing market share trends. The feature vector merging process organizes features from different sources into unified-dimensional vectors. Each feature vector contains a timestamp, feature value, and feature type metadata. The final generated enterprise operational status dataset uses a columnar storage format, includes a time series index and multiple feature dimensions, and can be directly used by the analysis module.

[0066] The operational status analysis module performs multi-dimensional business status assessments based on the enterprise's operational status dataset. The module extracts three core business metrics: the cost-benefit ratio, calculated as the ratio of net profit to total cost, reflects the effectiveness of cost control; the order delivery delay rate, calculated by comparing the actual delivery time with the promised delivery time, with the difference expressed as a percentage; and the market share fluctuation rate, derived by analyzing the deviation of the current market share from historical benchmarks, with the fluctuation expressed as a percentage change.

[0067] The comparison of key business metrics with industry benchmark thresholds employs a dynamic deviation calculation method. Industry benchmark thresholds are obtained from a pre-built industry database containing typical metric value ranges for companies of different industries and sizes. The dynamic deviation calculation measures the degree of difference between the current metric value and the benchmark value using a standardized distance metric method. The calculation result is converted into a percentage, representing the relative degree to which the current metric deviates from the industry typical value.

[0068] Multi-indicator collaborative anomaly analysis employs a sliding window mechanism to process time series data. The sliding window has a fixed time span, and data points within the window participate in the collaborative analysis. A probabilistic model calculates the correlation between multiple indicators, analyzing the correlation patterns of indicator changes through the covariance matrix. When multiple indicators deviate simultaneously, the model identifies this collaborative anomaly pattern and calculates the probability of its joint occurrence. A dynamic weighting strategy assigns weight coefficients based on the historical performance of each indicator, with indicators having a higher historical anomaly frequency receiving higher weights and those having a lower frequency receiving lower weights. These weight coefficients are updated periodically to reflect the latest business situation. The final generated anomaly feature set includes anomaly type classification labels, impact assessment values, and probability scores, providing input for subsequent decision-making.

[0069] Example 2: See Figure 3 The decision tree generation module's processing flow begins with receiving anomaly feature sets. These sets contain structured data fields such as anomaly type identifiers, impact level parameters, and probability scores. The anomaly type identifiers employ a classification coding system, using a three-letter alphanumeric combination to identify different anomaly types. For example, cost anomalies correspond to the CT prefix identifier, delivery delay anomalies to the DL prefix identifier, and market volatility anomalies to the MV prefix identifier. The impact level parameters use a graded quantification system, divided into high, medium, and low threshold ranges. High level corresponds to a probability score above 70%, medium level to 30%-70%, and low level to below 30%. The module's built-in parsing engine extracts these parameters and uses feature vector transformation technology to convert discrete parameters into continuous numerical vectors. Each vector contains a multi-dimensional representation of type encoding, level value, and probability value.

[0070] The historical case database is stored using a relational database architecture, containing four main tables: case number, anomaly feature vector, handling measure record, and effect evaluation index. The matching process utilizes a similarity calculation engine, loading the current anomaly feature vector and comparing it with historical feature vectors in the case database. An improved cosine similarity algorithm is used, focusing on measuring the degree of matching in the anomaly type and impact level dimensions. The algorithm outputs a similarity score between 0 and 1, selecting historical cases with scores higher than 0.85 for inclusion in the candidate set. The effectiveness weights of the handling paths for candidate cases are calculated, with the weights derived from the effect evaluation index in the case database, which is obtained by normalizing the improvement rate of post-handling business indicators. Finally, the handling paths of the top five historical cases with the highest weights are extracted as the basis for decision-making branches.

[0071] When constructing a multi-level decision network, the system uses the anomaly root node as the starting point of the topology. The root node inherits the type identifier and level parameters of the anomaly feature set, forming the node's initial feature vector. The branch generation engine creates decision paths based on historical case matching results, with each path corresponding to a specific type of handling strategy. Cost anomalies generate cost control branches, including raw material substitution nodes and production efficiency optimization nodes; delivery delay anomalies generate logistics adjustment branches, including transportation route optimization nodes and warehousing strategy adjustment nodes; market fluctuation anomalies generate market response branches, including pricing strategy nodes and promotion plan nodes. Each branch node sets condition judgment logic; for example, the raw material substitution node in the cost control branch sets a supplier switching cost threshold condition. The network structure uses a depth-first traversal algorithm to establish the node hierarchy, with parent and child nodes connected by decision logic to form a tree-like topology. The visualization rendering component transforms the abstract network into graphical elements; root nodes are identified by red hexagons, decision nodes by blue rectangles, and execution nodes by green circles. Connection lines between nodes annotate the decision condition expressions, generating a decision tree architecture that can be zoomed and viewed on the interactive interface.

[0072] After the data compensation module starts, it scans the nodes of the decision tree architecture. The integrity detection algorithm traverses each decision branch node, checking the population status of the required data fields. The detection rules are based on a preset decision tree metadata model, which defines the data dimensions that various types of nodes must include. When a node is found to have missing financial data, it is marked as missing cash flow forecast and cost structure analysis fields; when a node is found to have insufficient supply chain information, it is marked as missing supplier reliability and logistics timeliness fields. The system automatically generates a list of missing fields and associates them with specific decision node numbers to form a compensation task queue.

[0073] External data source calls are implemented through a pre-built interface connector. The macroeconomic indicator database connects to the National Bureau of Statistics' open platform API, establishing a secure connection using the OAuth2.0 authentication protocol, and accesses data streams such as quarterly GDP growth rate, monthly CPI index, and industry prosperity index. The industry intelligence database connects to third-party commercial data services, obtaining real-time industry reports via the Web Service protocol, focusing on extracting information on competitor dynamics, raw material price trends, and policy and regulatory changes. Data retrieval parameters are dynamically generated based on the needs of decision tree nodes; for example, it automatically retrieves pricing change data for similar products for market fluctuation nodes. Supplementary data is mapped to decision tree nodes after format conversion; the mapping rule library contains over 300 field matching rules, such as mapping "year-on-year CPI index" to the "cost forecast" field.

[0074] The confidence boost value is calculated using a probabilistic inference model. The basic decision confidence is derived from the initial probability score of the decision tree node, which was calculated during the decision tree generation stage. After the supplementary data is imported, the model re-evaluates the reliability of the decision. A Bayesian network calculates the conditional probability that the supplementary data supports the correctness of the decision; for example, when macroeconomic data shows a downward market trend, the reliability probability of the cost control decision increases accordingly. The confidence boost value is quantified as a percentage change, and the result, together with the supplementary data source identifier and mapping rule number, constitutes the data compensation parameter set. This parameter set is organized in JSON format and contains three core field groups: decision node number, supplementary data content, and confidence boost value, and is transmitted to downstream modules via a message queue.

[0075] Example 3: See Figure 4 The execution parameter generation module receives the decision tree architecture and data compensation parameter set as input and starts the decision node sequence parsing engine. This engine applies a topology sorting algorithm to process the dependencies between decision tree nodes and identify the logical execution order from the root node to the leaf node. The sorting process establishes a node priority queue, automatically prioritizing high-impact nodes and processing low-impact nodes later. The sorting criteria include node depth, impact level, and time sensitivity markers, forming an ordered execution sequence list.

[0076] The resource allocation quantification process analysis has three core dimensions: Human resource allocation coefficient calculation considers the complexity of node tasks and skill matching. Complexity is assessed based on the number of branches in the decision tree node and the amount of data processed, while skill matching maps to the job competency matrix. Coefficient calculation uses a linear relationship model to transform task requirements into equivalent human resource demand units. Fund allocation ratio calculation integrates node criticality and execution cost. Criticality is quantified by the influence level parameter, and execution cost is estimated through a historical case database. Ratio allocation applies an optimization algorithm to balance global budget constraints, generating a ratio allocation matrix. Execution time window threshold settings are based on task type attributes; strategic decisions have a wider time range, while operational tasks have stricter time boundaries. Threshold calculation references business cycle characteristics, such as the requirement to complete relevant decision execution before the end of the financial cycle.

[0077] The fusion process of time parameters and data compensation parameter sets employs a weighted integration method. The confidence boost value provided by the data compensation parameter set is converted into a priority adjustment coefficient, calculated using the following formula:

[0078] W n =α·I n +β·C n

[0079] Among them: W n Indicates the final weight of the node; I n This is the original impact level value; C n The confidence level is expressed as a percentage; α and β are adjustment coefficients for the impact level and confidence level, respectively. The adjustment coefficients are dynamically configured based on the decision type: strategic decisions prioritize confidence (β = 0.7), while executive decisions prioritize impact level (α = 0.8). Recalculating the weights triggers a secondary sorting of the execution sequence, forming a strategy execution parameter package. This parameter package uses a matrix storage format and includes a node execution sequence, a human resource allocation table, a funding allocation matrix, and a time window lookup table.

[0080] Once the dynamic correction module is activated, a real-time data monitoring channel is established. The module continuously receives updated enterprise operational status datasets from the operational data collection module, focusing on extracting market mutation indicators. The mutation indicator identification mechanism employs sliding window difference analysis, monitoring three core dimensions: competitor price volatility (calculating the percentage deviation between real-time prices and historical average prices); supply chain disruption index (quantifying logistics delays and abnormal inventory fluctuations); and market demand mutation value (capturing abnormal order volume trends). The monitoring frequency is dynamically adjusted based on decision criticality, with high-priority decision areas scanned every minute.

[0081] The environmental conflict detection engine compares the strategy execution parameter package with real-time environmental parameters. Conflict degree calculation employs multi-dimensional difference analysis: the deviation rate between the time window conflict detection execution progress and the planned timeline; the difference between actual resource consumption and planned allocation in resource allocation conflict analysis; and the mismatch value between preset conditions and real-time market status in environmental adaptation conflict assessment. The difference value is normalized to a conflict index in the range of 0-1, and an index exceeding 0.6 triggers a correction process.

[0082] The node weights are dynamically adjusted using an incremental update model. The distribution of decision tree node weights is recalculated based on the conflict index, with the adjustment factor δ positively correlated with the conflict index. Node weight increases in high-conflict areas follow an exponential curve, while low-conflict areas use linear adjustments. Resource allocation path optimization employs a rerouting mechanism: fund flow reallocation closes funding channels in conflict areas and opens backup funding paths; human resource scheduling is adjusted based on task delay status, reallocating personnel to lagging nodes. The dynamic correction engine generates an incremental update package every 5 minutes, containing a conflict index distribution map, a weight correction parameter table, and resource path change records, ultimately outputting a strategy optimization correction set. This set is managed using a version control format, with each version recording a timestamp, change summary, and impact assessment data.

[0083] Example 4: See Figure 5 Suppose a manufacturing company faces an anomaly in product delivery delays. The system has generated a strategy optimization correction set and an execution parameter package. After the resource allocation module starts, it parses the cross-departmental collaboration requirement identifiers in the correction set. These identifiers use structured encoding; for example, "PD-WH-SD-001" represents a first-level collaboration requirement between the production, warehousing, and sales departments. The module automatically identifies the department code and collaboration level in the identifiers and maps them to the company's organizational structure tree. The production department corresponds to the product assembly line resource pool, the warehousing department corresponds to the regional distribution center network, and the sales department corresponds to the regional distribution channel system.

[0084] The departmental execution domain division employs a spatial clustering algorithm. Taking the South China regional business unit as an example, the module limits the production execution domain to the Dongguan factory based on the geographical attributes of the tasks, the warehousing execution domain to the coverage area of ​​the Shenzhen distribution center, and the sales execution domain to the retail networks in Guangzhou and Shenzhen. Execution domain boundaries are set using GIS coordinates, and resources within each domain form an independent management unit. Resource scheduling time series calculation first decomposes collaborative tasks: the production domain receives the "accelerate material preparation" instruction and initiates the raw material procurement sub-task; the warehousing domain triggers the "inventory rebalancing" instruction and calculates the regional allocation volume; the sales domain executes the "delivery commitment adjustment" instruction and renegotiates the customer's time window. Time series modeling adopts an event-driven mechanism. See Table 1 for a breakdown of resource scheduling time series segments.

[0085] Table 1: Time series segments of resource scheduling.

[0086]

[0087]

[0088] The optimization of the matching degree between capital flow and logistics adopts dual-track tracking technology. The capital flow channel marks transaction flows through the enterprise payment system, while the logistics channel collects cargo displacement data through IoT (Internet of Things) devices. The matching engine compares capital release nodes and cargo movement nodes every minute: when raw material procurement funds are paid, the system simultaneously verifies the supplier's shipment scan record; when inventory transfer funds are allocated, it automatically associates the GPS trajectory of the transport vehicle. The matching degree parameter calculation uses a discrete event alignment algorithm, performing timestamp calibration on capital-logistics event pairs within each resource scheduling cycle, with deviations controlled within ±2 hours considered valid matches. The optimized dynamic resource allocation scheme outputs an XML document containing a departmental task topology diagram, a resource dependency matrix, and a real-time matching monitoring interface.

[0089] The operational metric generation module then initiates the processing flow. This module connects to the dynamic resource allocation scheme and strategy execution parameter package, first extracting the resource utilization curve. Taking the production department as an example, the curve data sources include: production line operation time series collected by equipment sensors, work hour utilization rate recorded by the human resources system, and raw material turnover rate provided by the material management system. The three data streams are aligned and merged through the time axis to generate a comprehensive resource utilization curve, with the horizontal axis representing the time scale (divided by hours) and the vertical axis representing the utilization percentage value. The module automatically marks key nodes: a utilization peak of 92% occurs at D210:00, and a trough of 65% occurs at D314:00.

[0090] A three-dimensional coordinate system is established to link the target achievement time nodes. The time dimension extracts planned milestones from the execution parameter package, such as "raw materials arrive D312:00"; the spatial dimension maps logistics nodes in the resource allocation plan; and the indicator dimension is associated with health indicator thresholds. The three dimensions intersect in the spatiotemporal matrix to form a verification point: at the D312:00 coordinate position, the system verifies the actual arrival scan record, compares it with the planned time deviation rate, and detects the magnitude of the event's impact on the resource utilization curve.

[0091] The cash flow health index calculation integrates multi-source data: real-time bank account balances, accounts receivable aging distribution, and accounts payable due date calendar. The index calculation uses a stress test model to simulate cash flow scenarios over the next 7 days, outputting a health score from 0 to 100. Customer satisfaction trend values ​​are collected from three channels: star ratings from the online rating system, sentiment analysis results from customer service calls, and return rate change curves. The data fusion engine normalizes the scattered data into a standardized satisfaction index, updating the trend every minute.

[0092] The enterprise operational health data table is constructed using a star schema. The time dimension table includes continuous timestamps and workday markers; the indicator fact table records sampled values ​​every minute; and the department dimension table links resource allocation relationships. Typical data records are as follows: the timestamp "D315:30" is associated with a production department resource utilization rate of 82%, a warehousing department logistics matching degree of 0.89, and a sales department satisfaction index of 76; the cash flow health index shows a score of 81; the system automatically marks this point as the milestone of "emergency procurement task closed." The data table output is in columnar storage format, generating a health status snapshot containing over 300 fields every minute, supporting time-range slice queries and multi-dimensional drill-down analysis.

[0093] Example 5: This example focuses on the processing mechanism of the feedback optimization module. This module receives an enterprise operational health data table as input. The data table structure includes a timestamp sequence, departmental dimension identifiers, and a multi-indicator numerical matrix. The module's built-in deviation detection engine initiates a time window scan, with the window width configured as daily, weekly, or monthly granularity based on the business scenario. The detection process locks onto a set of core performance indicators, including the cost-benefit ratio baseline, order delivery delay rate threshold, and market share target value. The scanning algorithm progresses along the time axis, calculating the algebraic difference between the current indicator value and the preset target value at each window position, forming a deviation data stream. When the deviation of a specific indicator exceeds the tolerance range for three consecutive time windows, the system marks that indicator as entering a continuous deviation state.

[0094] The continuous periodic measurement employs breakpoint detection technology. The engine records the start time stamp when the indicator first exceeds the tolerance boundary and continuously tracks it until the indicator returns to the normal range. The period length is accurate to the minute and stored as tuple data of start-end time pairs. Fluctuation amplitude feature extraction uses extreme value analysis to capture the highest and lowest points of the indicator's trajectory within the continuous period. The amplitude value is calculated as the absolute difference between the peak and trough values, while simultaneously recording the fluctuation frequency parameter, i.e., the number of times the average line is crossed per unit time. The feature extraction results form a structured feature vector containing the period length value, fluctuation amplitude value, frequency count, and deviation direction indicator.

[0095] The strategy execution feedback report employs a layered document architecture. The main report area presents a global overview, displaying a list of indicators in a state of continuous deviation and an assessment of their impact. The detailed analysis area is organized by department, with each department's report containing three sub-modules: a deviation characteristic table displaying the periodic and magnitude data of the department's core indicators; a time trend chart rendering the deviation change curve, overlaid with a target value reference line; and a related decision tree node mapping showing the original decision path that triggered the department's strategy execution. The report appendix includes details of data sampling points and feature calculation logs. The document generation process automatically applies corporate visual standards, uses differentiated color schemes for departmental data, and adds dynamic warning markers to key anomalies.

[0096] The parameter adjustment interface for the Operation Status Analysis module is implemented through the Configuration Management Center. This center stores historical versions of the threshold values ​​for calculating the deviation of key business indicators, with the currently effective threshold marked as active. The Feedback Optimization module submits threshold change requests to the Management Center, with the request payload including the target indicator code, suggested threshold range, and a summary of the basis for the change. The change is based on the statistical value of the continuous deviation of the corresponding indicator in the feedback report, referencing the policy execution. The threshold update algorithm uses a moving average model, with the new threshold undergoing a trend-adaptive shift based on the original value. For example, if the initial threshold for order delivery delay rate is set at 15%, and the feedback report shows that the average actual delay rate for two consecutive weeks reaches 18.7%, the system automatically calculates the adjustment amount, rounds it to the nearest integer, and updates the threshold to 18%, while retaining historical versions for retrospective comparison.

[0097] The dynamic weighting strategy employs a feedback learning mechanism to update weight allocation parameters. A weight library stores the currently effective weight matrix, with rows corresponding to business indicator categories and columns associated with different decision-making scenarios. Update triggers monitor the fluctuation amplitude characteristic values ​​in the strategy execution feedback report; when the fluctuation amplitude of a specific indicator exceeds the stability boundary, a weight recalculation process is initiated. A weight adjuster analyzes the fluctuation contribution rate of the indicator in the most recent three decision-making cycles, with the contribution rate calculated based on the covariance matrix decomposition results. New weight values ​​use a damped adjustment mode to avoid system oscillations caused by drastic changes. Weight matrix version management uses timestamps; each update generates a change log recording the adjusted indicator, original weight value, new weight value, and effective date.

[0098] The parameter synchronization mechanism employs a dual-channel verification mode. Threshold and weight change instructions are first written to a temporary storage area, and simultaneously a parameter preloading request is sent to the operational status analysis module. The analysis module processes historical data samples using the new parameters in a sandbox environment and outputs a simulation analysis report. The feedback optimization module compares the consistency score between the simulation report and the actual business results; once the score meets the acceptance criteria, an activation command is sent. Full parameter updates are executed during business downtime, with the change window controlled within 15 minutes. The new parameters, once activated, are applied to the data stream processing pipeline in real time, and subsequent input operational status datasets are analyzed and calculated using the updated thresholds and weights. The system retains a parameter rollback function, automatically triggering a version rollback procedure when abnormal fluctuations occur for five consecutive analysis periods.

[0099] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0100] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent analysis system for enterprise operation data based on artificial intelligence, characterized in that, include: The operational data acquisition module is used to collect enterprise financial data flow, supply chain logistics data and market dynamic data in real time, integrate multi-source heterogeneous data and perform data cleaning to generate enterprise operational status datasets; The operational status analysis module, based on the enterprise operational status dataset, identifies the deviation between key business indicators and preset thresholds, calculates multi-dimensional business status anomalies through a dynamic weighting strategy, and generates an anomaly feature set. The decision tree generation module constructs a multi-level decision path based on the abnormal feature set, matches the optimal decision branch with the historical handling case library, and generates a visual decision tree architecture. The data compensation module, based on the decision tree architecture and the abnormal feature set, analyzes the impact of missing data on the completeness of decision-making, automatically supplements external data sources related to the business scenario, and generates a data compensation parameter set. The execution parameter generation module, based on the decision tree architecture and data compensation parameter set, parses the priority of execution nodes in the decision path, quantifies the resource allocation ratio and time window parameters, and generates a strategy execution parameter package.

2. The intelligent analysis system for enterprise operation data based on artificial intelligence according to claim 1, characterized in that, The specific details of generating the enterprise operation status dataset are as follows: Configure financial data collection interfaces, supply chain data collection interfaces, and market data collection interfaces to capture real-time transaction volume, inventory turnover rate, and market competition index, respectively. Perform timestamp alignment on multi-source heterogeneous data, remove outliers and fill in missing fields; The cleaned data is integrated through a data feature fusion engine to generate a dataset of enterprise operational status.

3. The intelligent analysis system for enterprise operation data based on artificial intelligence according to claim 2, characterized in that, The specific steps for generating the anomaly feature set are as follows: Extract cost-benefit ratio, order delivery delay rate, and market share fluctuation values ​​from the enterprise operation status dataset; Calculate the dynamic deviation of key business indicators from industry benchmark thresholds; A sliding window mechanism is used to analyze the probability of multiple indicators working together to generate an anomaly feature set.

4. The intelligent analysis system for enterprise operation data based on artificial intelligence according to claim 3, characterized in that, The specific architecture for generating a visual decision tree is as follows: Parse the anomaly type identifier and impact level parameter in the anomaly feature set; Match the effectiveness weight of the handling path for the same type of anomaly in the historical case database; Construct a multi-branch decision network starting from the abnormal root node to generate a decision tree architecture.

5. The intelligent analysis system for enterprise operation data based on artificial intelligence according to claim 4, characterized in that, The generated data compensation parameter set is specifically as follows: Identify decision branch nodes in the decision tree architecture that lack data integrity; Supplement external correlation factors by accessing macroeconomic indicator databases and industry intelligence databases; The improvement in decision confidence by supplementary data is quantified, and a set of data compensation parameters is generated.

6. The intelligent analysis system for enterprise operation data based on artificial intelligence according to claim 5, characterized in that, The specific parameter package for the generation strategy execution is as follows: Analyze the execution sequence of key decision nodes in the decision tree architecture; Quantify human resource allocation coefficients, funding allocation ratios, and execution time window thresholds; The associated data compensation parameter set optimizes the execution priority configuration and generates a strategy execution parameter package.

7. The intelligent analysis system for enterprise operation data based on artificial intelligence according to claim 6, characterized in that, Also includes: The dynamic correction module, based on the strategy execution parameter package and the real-time updated enterprise operation status dataset, monitors environmental variable disturbances during execution, dynamically adjusts the execution node weights and resource allocation paths, and generates a strategy optimization correction set; the process of generating the strategy optimization correction set is as follows: Real-time acquisition of market mutation indicators from updated enterprise operational status datasets; Detect the degree of conflict between the strategy execution parameter package and the real-time environment parameters; The execution weights of decision nodes are recalculated and resource allocation paths are adjusted to generate a strategy optimization correction set.

8. The intelligent analysis system for enterprise operation data based on artificial intelligence according to claim 7, characterized in that, Also includes: The resource allocation module, based on the strategy optimization and correction set and the execution parameter package, divides the business department execution domain, calculates the cross-departmental collaborative resource scheduling sequence, and generates a dynamic resource allocation scheme; the process of generating the dynamic resource allocation scheme is as follows: Parse the cross-departmental collaboration requirement identifiers in the strategy optimization and correction set; Calculate the time series of resource scheduling for the production, warehousing, and sales departments; Optimize the matching parameters between cash flow and logistics to generate dynamic resource allocation schemes.

9. The intelligent analysis system for enterprise operation data based on artificial intelligence according to claim 8, characterized in that, Also includes: The operational metric generation module, based on the aforementioned dynamic resource allocation scheme and strategy execution parameter package, extracts the change trajectory of key performance indicators and generates an enterprise operational health data table by associating it with the time dimension. The process of generating the enterprise operational health data table is as follows: Extract the resource utilization rate curve from the aforementioned dynamic resource allocation scheme; The target achievement time point in the associated strategy execution parameter package; By integrating the cash flow health index and customer satisfaction trend value, a data table on the health of enterprise operations is generated.

10. The intelligent analysis system for enterprise operation data based on artificial intelligence according to claim 9, characterized in that, Also includes: The feedback optimization module, based on the enterprise operational health data table, identifies the continuous deviation between key performance indicators and preset target values, extracts the characteristics of the deviation duration and fluctuation range, and generates a strategy execution feedback report. Based on the strategy execution feedback report, adjust the threshold for calculating the deviation of key business indicators in the operation status analysis module, and update the weight allocation parameters of the dynamic weighted strategy.

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