Enterprise digital operation real-time data analysis method based on big data

Abstract

The invention discloses an enterprise digital operation real-time data analysis method based on big data, and particularly relates to the field of data analys.The enterprise digital operation real-time data analysis method comprises the steps that enterprise operation data are collected in real time through a multi-protocol adapter, abnormity is dynamically cleaned and detected, and data standardization is achieved in combination with statistical process control and BERT sentiment analysis; in the hierarchical stream processing architecture, edge nodes pre-aggregate data and filter anomalies, a center engine dynamically divides time windows to generate minute-level / second-level indexes, and a self-adaptive rolling window and dual-threshold session cutting support inventory monitoring and user behavior analysis; the incremental machine learning model updates prediction in real time based on a sliding window, screens a dynamic optimization decision tree through Hoeffering splitting conditions and feature importance, and triggers fine tuning or full training when data distribution drift is detected; and the cross-source data association engine fuses real-time and historical data, calculates a KPI anomaly score, and triggers hierarchical early warning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, more particularly, the present application relates to a real-time data analysis method for enterprise digital operation based on big data. BACKGROUND

[0002] In today's digital era, enterprises are facing unprecedented changes and challenges. With the rapid development of information technology, digital transformation has become the only way for enterprises to improve competitiveness and achieve sustainable development. Enterprises have integrated digital technology into various aspects of operation, from production and manufacturing to marketing, from supply chain management to customer service, striving to improve efficiency, reduce costs and optimize decision-making through digital means.

[0003] However, in the process of digital operation, enterprises face the problem of processing massive amounts of data. As the business of enterprises continues to expand and the degree of digitalization deepens, data from various business systems is growing explosively, including transaction records, customer behavior data, device operation data, etc., with various formats and complex sources.

[0004] Traditional data analysis methods often struggle to cope with such large-scale, high-frequency data updates, and cannot provide valuable insights to enterprises in a timely manner, resulting in decision-making lag and missed development opportunities when facing rapidly changing market environments and customer needs. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a real-time data analysis method for enterprise digital operation based on big data to solve the problems raised in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0007] Step A1: Real-time collection of enterprise operation data stream, including business system log, IoT device data, user interaction behavior and third-party platform data; dynamic configuration of data cleaning rules to denoise, fill missing values and detect anomalies for original data stream;

[0008] Step A2: Establish a hierarchical stream processing architecture:

[0009] The first layer of lightweight edge computing nodes performs data pre-aggregation and real-time filtering of key events;

[0010] The second layer of distributed stream processing engine generates minute / second statistical indicators according to business scenario requirements through time window dynamic division technology;

[0011] Step A3: Incremental machine learning model is used to update business index prediction online:

[0012] Dynamically train prediction models based on real-time data streams within sliding time windows;

[0013] When data distribution drift is detected, model fine-tuning is automatically triggered and historical weights are retained;

[0014] Step A4: Through the cross-source data association engine, real-time indicators are integrated with the historical data warehouse, and the KPI anomaly score is dynamically calculated based on the business rule engine to trigger a graded warning strategy.

[0015] Preferably, in step A1, the data acquisition end is connected to heterogeneous data sources through a multi-protocol adapter. For business system logs, the Filebeat+Logstash combination is used to capture the structured / semi-structured logs generated by the application server in real time, and key fields are extracted through a preset regular expression template; IoT device data is connected to the edge gateway through the MQTT protocol, and the gateway has a built-in lightweight verification module to filter invalid sensor readings; user interaction behavior data is collected through the tracking SDK, and after the basic event type classification is completed on the client, it is pushed to the message queue in JSON format; third-party platform data is obtained through API polling callback, and the OAuth2.0 token dynamic refresh mechanism is used to maintain long-term connection stability.

[0016] Preferably, in step A2, during the implementation of the layered stream processing architecture, an edge-center collaborative computing model is adopted; the first layer of lightweight edge computing nodes are deployed at the data source and run an embedded stream processing engine; the nodes load business predefined rule scripts and perform millisecond-level response operations on the original data stream;

[0017] Each node maintains a sliding pre-aggregation window to compress the original data stream: For numerical IoT sensor data, the following incremental calculation is performed by device group. The calculation method is as follows:

[0018] S t =S t-1 +x i -x i-w

[0019] Among them, S t It is expressed as the sum of the data points in the current window, S t-1 Expressed as the window sum of the previous moment, x i Represents the newly arrived data point, x i-w Represented as old data points that slide out of the window;

[0020]

[0021] Among them, μ t Expressed as the mean of the current window, S tS(w) = ∑x

[0022]

[0023] where, S2(w) = ∑(x k xk t where,

[0024] When a burst anomaly is detected, i.e., |x i - μ t | > 3σ t , a local alert is triggered immediately and only the anomaly event metadata is uploaded, reducing more than 90% of the uplink bandwidth consumption;

[0025] For user behavior clickstream, a Bloom filter is applied to quickly deduplicate repeated events, and a rule engine is used to match key behavior patterns in real time, triggering local alerts or data degradation. The edge node also has dynamic degradation capability—when network latency exceeds 50 ms, it automatically switches to local cache mode, discarding low-priority data according to QoS strategy to ensure reliable transmission of critical events.

[0026] Preferably, in step A3, the data stream enters a sliding feature window, and the samples in the window are dynamically processed through a feature pipeline. Numerical variables are standardized by Z-Score, categorical features are mapped to a fixed-dimensional embedding space through dynamic hash encoding, and text fields are updated with word vectors in real time through a lightweight Word2Vec model. Every 1000 new samples or 1 minute, i.e., when any condition is met, a round of incremental training is triggered—online random forest algorithm maintains a set of active decision trees;

[0027] When new data arrives, for each tree, only the new samples that reach its leaf nodes are recalculated, and when the split gain exceeds the threshold, a new branch is grown on the original tree structure;

[0028] For the leaf node L where the sample finally arrives, an exponential decay weighting algorithm is used to update the stored statistics; when the number of samples in the leaf node exceeds the threshold, calculate its split gain for all possible features and split points, and the calculation method is as follows:

[0029]

[0030] where, Gain(X j ,d) represents the split gain of the node under feature X j and split point d; V(L) represents the variance of the current leaf node L, |L| represents the number of samples in node L, and V(Lleft ) represents the variance of the left child node after splitting, V(L right ) represents the variance of the right child node after splitting, L left represents the left child node after splitting, L right represents the right child node after splitting; X j represents one of all possible features, d represents the split point;

[0031] The splitting is only performed when the Hoeffding splitting condition is met, and the calculation method of meeting the condition is specifically:

[0032]

[0033] wherein, represents the split gain of the optimal feature, represents the split gain of the suboptimal feature, ∈ represents the gain difference threshold value, R represents the target variable range, that is, the difference between the maximum value and the minimum value of the target variable; δ represents the confidence parameter, n L represents the number of samples of the current leaf node;

[0034] After splitting, the weight of the historical branch is frozen, that is, the statistics thereof is no longer updated, and only the new branch participates in subsequent learning.

[0035] Preferably, in the step A4, the real-time data stream and the historical data warehouse are associated through a space-time alignment engine:

[0036]

[0037] wherein, represents a fuzzy join operation based on a business primary key and a time tolerance, key represents the business primary key, τ represents the time tolerance, represents an output enhanced view, represents a real-time data stream, represents a subset of a historical data warehouse, Δt2 represents a time range offset of historical data;

[0038] The enhanced data stream after association adopts a hybrid strategy for dynamic KPI calculation, and the calculation method of the real-time aggregation index is specifically:

[0039]

[0040] wherein, represents a real-time aggregation index, r i represents an original index value, that is, the value of the i th data point; q i represents a time decay weight, C represents the total number of data points, and the number of original index values participating in calculation;

[0041]

[0042] wherein, is expressed as a trend comparison index, is expressed as a real-time aggregation index, μ t,history is expressed as a historical same period mean, σ t,history is expressed as a historical same period standard deviation;

[0043] An abnormal value is calculated for each KPI, and the calculation method of the abnormal value is specifically:

[0044]

[0045] wherein, Q represents an abnormal value, v represents a business type, v1 represents a business impact degree, v2 represents a trend deviation degree, v3 represents a data confidence degree, U1 represents a business impact degree weight, U2 represents a trend deviation degree weight, and U3 represents a data confidence degree weight;

[0046] According to the abnormal score Q, a three-level response is triggered, when the calculated abnormal value ranges in [0.5, 0.7), an email notification alarm is triggered; when the calculated abnormal value ranges in [0.7, 0.9), an SMS alarm is triggered; and when the calculated abnormal value Q is greater than or equal to 0.9, an interface automatic repair is triggered.

[0047] Technical effects and advantages of the present application:

[0048] The present application collects business system logs, IoT device data, user interaction behaviors and third-party platform data in real time through a multi-protocol adapter, and dynamically configures data cleaning rules for denoising, missing value filling and anomaly detection. Secondly, a hierarchical stream processing architecture is established, the first layer edge computing node performs data pre-aggregation and key event filtering, and the second layer distributed stream processing engine generates multi-granularity statistical indicators; then, an incremental machine learning model is used for online updating of business index prediction, dynamic adjustment of feature set and response to data distribution drift; finally, through a cross-source data correlation engine, real-time indicators and historical data are fused, KPI abnormal score is dynamically calculated, and a hierarchical early warning strategy is triggered.

[0049] The present application can cope with large-scale and high-frequency data updates through the above-mentioned scheme, and provide valuable data for enterprises in a timely manner, so that enterprises can make decisions in advance when facing rapidly changing market environment and customer demand. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 is a method flowchart of the present application.

[0051] Figure 2 is a schematic diagram of establishing a shunt stream processing structure in step A2 of the present application. is a schematic diagram of establishing a shunt stream processing structure in step A2 of the present application.

[0052] Figure 3 This is a schematic diagram of business indicator prediction in step A3 of the present invention. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] See also Figure 1 As shown, the present invention provides a real-time data analysis method for enterprise digital operations based on big data, comprising the following steps:

[0055] Step A1: Real-time collection of enterprise operational data streams, including business system logs, IoT device data, user interaction behavior, and third-party platform data. Dynamic configuration of data cleansing rules to denoise the raw data streams, fill in missing values, and detect anomalies.

[0056] In step A1, the data collection end connects to heterogeneous data sources through a multi-protocol adapter. For business system logs, a combination of Filebeat and Logstash is used to capture structured / semi-structured logs generated by the application server in real time, and key fields are extracted using a preset regular expression template. IoT device data is connected to the edge gateway via the MQTT protocol, and the gateway has a built-in lightweight verification module to filter invalid sensor readings. User interaction behavior data is collected through the tracking SDK and pushed to the message queue in JSON format after basic event type classification is completed on the client. Third-party platform data is obtained through API polling callback, and the OAuth2.0 token dynamic refresh mechanism is used to maintain long-term connection stability.

[0057] Among them, Filebeat is a lightweight log forwarder installed on the server where logs are to be collected; Logstash is a data processing pipeline tool; MQTT is a lightweight message transmission protocol based on the publish-subscribe model;

[0058] In the dynamic data cleaning process, the metadata structure of the input data stream is parsed by the Schema automatic inference engine, different data sources generate adaptive type checking templates, structured data exported by relational databases directly map field types; semi-structured JSON logs automatically identify nested fields and build tree-shaped checking paths; and binary data streams uploaded by IoT devices are deserialized according to the pre-registered Protocol Buffer description file and then verified for field integrity. When a type conflict is detected, a repair strategy is automatically triggered - numerical fields are replaced with a median value, and character fields apply the Levenshtein distance algorithm to match the closest valid value.

[0059] The anomaly detection link dynamically generates threshold boundaries using statistical process control techniques: for time series indicators, the moving average and standard deviation within the sliding window are calculated in real time, the next period's reasonable range [μ-2.5σ, μ+2.5σ] is predicted through an exponential weighted moving average model, and data points outside this range are marked as anomalies; for discrete events, a Poisson distribution model is established, and when the number of events per unit time exceeds

[0060] The unstructured text processing chain integrates a real-time sentiment analysis unit, user review / work order text is scored for sentiment polarity through a pre-trained BERT micro model, and conditional random field entity recognition is used to extract product model and fault code key entities; the domain vocabulary is dynamically updated - when a newly appearing entity is detected with a frequency exceeding a threshold, an online active learning process is automatically triggered, the model is quickly iterated through a manual annotation platform, and the cleaned structured entities and sentiment labels are finally written to an Elasticsearch index, supporting real-time multi-dimensional aggregation analysis.

[0061] Step A2: Establish a hierarchical stream processing architecture:

[0062] The first layer of lightweight edge computing nodes performs data pre-aggregation and real-time filtering of key events;

[0063] The second layer of distributed stream processing engines generates minute / second-level statistical indicators according to business scenario requirements through time window dynamic division techniques;

[0064] In the implementation of the hierarchical stream processing architecture in step A2, an edge-center collaborative computing model is used; the first layer of lightweight edge computing nodes are deployed at the data source, running embedded stream processing engines; the nodes load business pre-defined rule scripts to perform millisecond-level response operations on raw data streams;

[0065] ​Each node maintains a sliding pre-aggregation window to compress the original data stream: For numerical IoT sensor data, the following incremental calculation is performed by device group. The calculation method is as follows:

[0066] S t =S t-1 +x i -x i-w

[0067] Among them, S t It is expressed as the sum of the data points in the current window, S t-1 Expressed as the window sum of the previous moment, x i Represents the newly arrived data point, x i-w Represented as old data points that slide out of the window;

[0068]

[0069] Among them, μ t Expressed as the mean of the current window, S t It is represented as the sum of data points in the current window, and w is the window size;

[0070]

[0071] in, Expressed as the current window variance, x k Represented as the kth data point in the window, μ t It is represented as the mean of the current window, and w is represented as the window size;

[0072] When a sudden abnormality is detected, that is, |x i -μ t |>3σ t When a local alarm is triggered, only the metadata of the abnormal event is uploaded, reducing uplink bandwidth consumption by more than 90%;

[0073] Bloom filters are applied to user behavior clickstreams to quickly deduplicate repeated events, and key behavior patterns are matched in real time based on the rule engine to trigger local alarms or data degradation. Edge nodes also have dynamic degradation capabilities - when network delays exceeding 50ms are detected, they automatically switch to local cache mode and discard low-priority data according to QoS policies to ensure reliable transmission of key events.

[0074] The second-tier central stream processing engine receives aggregated data streams uploaded by edge nodes and uses a dynamic window partitioning algorithm for multi-granularity analysis. It automatically selects the window type based on business rules:

[0075] For scenarios such as inventory monitoring, the adaptive rolling window dynamically adjusts the window length based on data volatility. The calculation method is as follows:

[0076]

[0077] Wherein, T represents the time window, T min represents the minimum time window, T max represents the maximum time window, T base represents the basic time window, a represents the industry acceptable fluctuation coefficient, β t represents the window standard deviation, μ t represents the mean value of the current window.

[0078] In the session window, the user behavior analysis adopts a double-threshold session cutting:

[0079] If the time interval > T1, the current window is closed; T1 represents the inactivity time threshold; that is, the maximum allowed interval time between user behavior events; if the time interval of two consecutive events exceeds T1, it is considered that the current session is ended, and a new session is started;

[0080] If the window duration > T2, the session is forced to be split; T2 represents the maximum window duration threshold; that is, the maximum allowed duration of a session window; if the duration of the current window exceeds T2, the session is forced to be split, even if the user is still active;

[0081] When the number of events B in the window satisfies B > T3, the session is output; T3 represents the minimum event number threshold; that is, the minimum number of events that must be contained in a session window; if the number of events B in the window is less than T3, the session will not be output;

[0082] The two-layer architecture is coordinated through a dynamic back pressure mechanism; when the processing delay of the center layer exceeds the threshold, the edge node is automatically degraded to the local cache mode, and the state synchronization is performed after the network is restored.

[0083] Step A3: an incremental machine learning model is used to update the business index prediction online:

[0084] Based on the real-time data stream in the sliding time window, the prediction model is dynamically trained;

[0085] When the data distribution drift is detected, the model fine-tuning is automatically triggered and the historical weights are reserved;

[0086] In step A3, the data stream enters the sliding feature window, the samples in the window are dynamically processed through the feature pipeline, the numerical variables are standardized by Z-Score, the categorical features are mapped to the fixed-dimensional embedding space through dynamic hash coding, and the text fields are updated by the lightweight Word2Vec model. Every 1000 new samples or 1 minute, that is, any condition is met, a round of incremental training is started. The online random forest algorithm maintains a set of active decision trees;

[0087] When new data arrives, for each tree, only the new samples that arrive at its leaf nodes are recalculated, and when the split gain exceeds the threshold, a new branch is grown on the original tree structure;

[0088] For the leaf node L where the sample finally arrives, the stored statistics are updated using an exponential decay weighting algorithm; when the number of samples in the leaf node exceeds the threshold, calculate the split gain for all possible features and split points. The calculation method is as follows:

[0089]

[0090] Where Gain(X j ,d) represents the split gain of the node under feature X j and split point d; V(L) represents the variance of the current leaf node L, |L| represents the number of samples in node L, V(L left ) represents the variance of the left child node after splitting, V(L right ) represents the variance of the right child node after splitting, L left represents the left child node after splitting, L right represents the right child node after splitting; X j represents one of all possible features, and d represents the split point;

[0091] Only when the Hoeffding split condition is met, the split is performed. The calculation method of meeting the condition is as follows:

[0092]

[0093] Where, represents the split gain of the optimal feature, represents the split gain of the suboptimal feature, ∈ represents the gain difference threshold, R represents the target variable range, that is, the difference between the maximum and minimum values of the target variable; δ represents the confidence parameter, n L represents the number of samples in the current leaf node;

[0094] After splitting, the weights of the historical branches are frozen, that is, their statistics are no longer updated, and only the new branches participate in subsequent learning.

[0095] Dynamic feature importance screening, evaluate the feature contribution every 10 minutes, if the importance of feature X j is less than the threshold for 3 evaluation periods, remove the feature, add the newly detected significant feature; evaluate the feature importance every 10 minutes, dynamically adjust the feature set: for feature X j , its importance I j is calculated as the average variance reduction brought by the feature in all trees, and the calculation method is as follows:

[0096]

[0097] where I j represents the importance of feature X j , M represents the total number of trees, T m represents the mth tree, n L represents the number of samples in the current leaf node, Gain(X j , L) represents the split gain of feature X j on leaf node L, and N represents the total number of samples.

[0098] Detect new features, if the mutual information between the univariate and the target variable exceeds the threshold, add the feature to the model and initialize the related split rule, and the calculation method of mutual information is as follows:

[0099]

[0100] where MI(X new , Y) represents the mutual information between the new feature and the target variable, X new represents the newly emerged feature, g represents the value of the new feature, h represents the value of the target variable, p(g, h) represents the joint probability, p(g) represents the marginal probability distribution of the new feature, and p(h) represents the marginal probability distribution of the target variable.

[0101] If the model error rises by more than 10% after introducing the new feature, roll back to the last stable version.

[0102] Continuously monitor the prediction shift index, when the distribution difference between the actual value and the predicted value in the window exceeds 0.25, it is determined that data distribution drift has occurred, when 0.25 < PSI ≤ 0.4, it is determined that mild drift has occurred; when PSI > 0.4, it is determined that severe drift has occurred; PSI represents the shift index; at this time, a two-level response mechanism is triggered:

[0103] Mild drift: start the model fine-tuning mode, retain 75% of the high-weight trees in the forest, retrain the remaining 25% of the trees with new data, and reduce the learning rate to 1 / 10 of the original value to prevent overfitting.

[0104] Severe drift: switch to ensemble learning mode, weight the prediction results of real-time model and the output of offline baseline model, and dynamically adjust the weight according to real-time test; at the same time, start the background full training task, and replace the current version seamlessly after the new model is verified.

[0105] When the prediction error rate of the real-time model exceeds the SLA threshold for 5 consecutive times, automatically roll back to the last stable version and issue an operation and maintenance alert.

[0106] Step A4: Through the cross-source data association engine, fuse real-time indicators and historical data warehouse, and dynamically calculate KPI anomaly scores based on the business rule engine to trigger a hierarchical warning strategy.

[0107] In step A4, the real-time data stream and the historical data warehouse are associated through a space-time alignment engine:

[0108]

[0109] Wherein, represents a fuzzy join operation based on business primary keys and time tolerance, key represents a business primary key, and tau represents a time tolerance, represents an output enhanced view, represents a real-time data stream, represents a subset of a historical data warehouse, and Delta t2 represents a time range offset of historical data;

[0110] The associated enhanced data stream adopts a hybrid strategy for dynamic KPI calculation, and the calculation method of the real-time aggregated indicator is as follows:

[0111]

[0112] Wherein, represents a real-time aggregated indicator, r i represents an original indicator value, i.e., the value of the i-th data point; q i represents a time decay weight, C represents the total number of data points, and the number of original indicator values participating in the calculation;

[0113]

[0114] Wherein, represents a trend comparison indicator, represents a real-time aggregated indicator, mu t,history represents a historical same period mean, sigma t,history represents a historical same period standard deviation;

[0115] For each KPI calculation anomaly value, the calculation method of the anomaly value is as follows:

[0116]

[0117] Wherein, Q represents an abnormal value, v represents a business type, v1 represents a business impact degree, v2 represents a trend deviation degree, v3 represents a data confidence, U1 represents a business impact degree weight, U2 represents a trend deviation degree weight, and U3 represents a data confidence weight;

[0118] The calculation method of the business impact degree is specifically as follows:

[0119]

[0120] Wherein, v1 represents a business impact degree, ΔKPI represents a KPI change amount, KPI baseline represents a benchmark KPI, and U1 represents a business impact degree weight;

[0121] For example, the current KPI value KPI t = 150

[0122] The historical same-period benchmark KPI value KPI t-1h = 100

[0123] The business weight = 1.0

[0124] The calculation steps are as follows:

[0125] The KPI change amount is calculated: ΔKPI = 150-100 = 50

[0126] The absolute value is taken: |ΔKPI| = 50

[0127] The standardized change amount is calculated:

[0128] The business impact degree is calculated: v1 = 0.5×1.0 = 0.5

[0129] Impact = 0.5 indicates that the current KPI is 50% higher than the historical same period, and the impact degree on the business is moderate.

[0130] The calculation method of the trend deviation degree is specifically as follows:

[0131]

[0132] Wherein, v2 represents a trend deviation degree, represents a trend comparison index, represents a cumulative sum, z φ / 2 represents a standard normal distribution quantile, θ cusum represents a cumulative sum threshold;

[0133] For example, the current KPI value KPI t = 120

[0134] Historical trend value Trend = 100

[0135] Standard normal distribution quantile z φ / 2 = 1.96

[0136] Cumulative sum

[0137] Cumulative sum threshold θ cusum = 20

[0138] Calculation steps:

[0139] Calculate the absolute deviation of the current KPI from the trend:

[0140] Standardized deviation:

[0141] Standardized cumulative sum:

[0142] Take the maximum value: Deviation = max (10.20, 1.5) = 10.20

[0143] Result analysis: Deviation = 10.20 indicates that the current KPI deviates from the trend to a higher degree and has statistical significance;

[0144]

[0145] where v3 represents the data confidence, u1 represents the field missing rate in the data, u2 represents the sample anomaly rate in the data, and u3 represents the model prediction uncertainty value;

[0146] Trigger a three-level response according to the anomaly score Q. When the calculated anomaly value range is [0.5, 0.7), trigger email notification alarm; when the calculated anomaly value range is [0.7, 0.9), trigger SMS alarm; when the calculated anomaly value Q ≥ 0.9, trigger interface automatic repair;

[0147] The dashboard integrates the following components:

[0148] Heat map matrix: shows the two-dimensional distribution of each KPI;

[0149] Drift detection chart: shows the relationship with the threshold line;

[0150] Data bloodline tracking: click on the abnormal KPI to drill down to the original data link.

[0151] Finally: the above only for the preferred embodiments of the present application, and not for limiting the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application, should be included in the scope of protection of the present application.

Claims

1. A real-time data analysis method for enterprise digital operations based on big data, characterized in that: include: Step A1: Real-time collection of enterprise operational data streams, including business system logs, IoT device data, user interaction behavior, and third-party platform data. Dynamic configuration of data cleaning rules to denoise the raw data streams, fill in missing values, and detect anomalies. Step A2: Establish a layered stream processing architecture: The first layer of lightweight edge computing nodes performs data pre-aggregation and real-time filtering of key events; The second-tier distributed stream processing engine uses dynamic time window partitioning technology to generate minute-level / second-level statistical indicators based on business scenario requirements; Step A3: Use incremental machine learning models to update business indicator predictions online: Dynamically train prediction models based on real-time data streams within sliding time windows; When data distribution drift is detected, model fine-tuning is automatically triggered and historical weights are retained; Step A4: Through the cross-source data association engine, real-time indicators are integrated with the historical data warehouse, and the KPI anomaly score is dynamically calculated based on the business rule engine to trigger a graded warning strategy.

2. The method for real-time data analysis of enterprise digital operations based on big data according to claim 1, characterized in that: In step A1, the data collection end connects to heterogeneous data sources through a multi-protocol adapter. For business system logs, a combination of Filebeat and Logstash is used to capture structured / semi-structured logs generated by the application server in real time, and key fields are extracted using a preset regular expression template. IoT device data is connected to the edge gateway via the MQTT protocol, and the gateway has a built-in lightweight verification module to filter invalid sensor readings. User interaction behavior data is collected through the tracking SDK and, after basic event type classification is completed on the client, pushed to the message queue in JSON format. Third-party platform data is obtained through API polling callback and uses the OAuth2.0 token dynamic refresh mechanism.

3. The method for real-time data analysis of enterprise digital operations based on big data according to claim 2, characterized in that: The anomaly detection phase uses statistical process control technology to dynamically generate threshold boundaries. For time series indicators, the moving average and standard deviation within the sliding window are calculated in real time. The exponentially weighted moving average model is used to predict the reasonable range of [μ-2.5σ, μ+2.5σ] for the next period. Data points outside this range are marked as anomalies. For discrete events, a Poisson distribution model is established. When the number of events per unit time exceeds When a traffic flow is detected, it is determined as a burst traffic flow, α is expressed as the historical mean, and all abnormal data are recorded with the original value and deviation score for subsequent root cause analysis.

4. The method for real-time data analysis of enterprise digital operations based on big data according to claim 1, characterized in that: In step A2, during the implementation of the layered stream processing architecture, an edge-center collaborative computing model is adopted; the first layer of lightweight edge computing nodes are deployed at the data source and run an embedded stream processing engine; the nodes load predefined business rule scripts and perform millisecond-level response operations on the original data stream; Each node maintains a sliding pre-aggregation window to compress the original data stream: For numerical IoT sensor data, the following incremental calculation is performed by device group. The calculation method is as follows: S t =S t-1 +x i -x i-w Among them, S t It is expressed as the sum of the data points in the current window, S t-1 Expressed as the window sum of the previous moment, x i Represents the newly arrived data point, x i-w Represented as old data points that slide out of the window; in, Expressed as the current window variance, x k Represented as the kth data point in the window, μ t It is represented as the mean of the current window, and w is represented as the window size; When a sudden abnormality is detected, that is, |x i -μ t |>3σ t When an abnormal event occurs, a local alarm is triggered immediately and only the abnormal event metadata is uploaded.

5. The method for real-time data analysis of enterprise digital operations based on big data according to claim 4, characterized in that: The second-tier central stream processing engine receives aggregated data streams uploaded by edge nodes and uses a dynamic window partitioning algorithm for multi-granularity analysis. It automatically selects the window type based on business rules: In the inventory monitoring scenario, the adaptive rolling window dynamically adjusts the window length according to the data volatility. The calculation method is as follows: Among them, T represents the time window, T min Expressed as the minimum time window, T max Expressed as the maximum time window, T base is the basic time window, a is the acceptable volatility coefficient of the industry, β t Expressed as window standard deviation, μ t Expressed as the mean of the current window.

6. The method for real-time data analysis of enterprise digital operations based on big data according to claim 1, characterized in that: In step A3, the data stream enters the sliding feature window, and the samples in the window are dynamically processed by the feature pipeline. Numerical variables are normalized by Z-Score, categorical features are mapped to a fixed-dimensional embedding space through dynamic hash coding, and text fields are updated with word vectors in real time through a lightweight Word2Vec model. For the leaf node L where the sample finally arrives, the exponential decay weighted algorithm is used to update its stored statistics; when the number of samples at the leaf node exceeds the threshold, its splitting gain is calculated for all possible features and splitting points. The calculation method is as follows: Among them, Gain(X j ,d) is expressed as in feature X j and the node splitting gain under the splitting point d; V(L) represents the variance of the current leaf node L, |L| represents the number of samples in the node L, V(L left ) is the variance of the left child node after splitting, V(L right ) is expressed as the variance of the right child node after splitting, L left Represents the left child node after splitting, L right Represents the right child node after splitting; X j It is represented as one of all possible features, and d is the split point; Splitting is performed only when the Hoeffding splitting condition is met. The calculation method for meeting the condition is as follows: in, Expressed as the split gain of the optimal feature, It is represented as the split gain of the suboptimal feature, ∈ is represented as the gain difference threshold, R is represented as the target variable range, that is, the difference between the maximum and minimum values ​​of the target variable; δ is represented as the confidence parameter, n L Indicates the number of samples of the current leaf node.

7. The method for real-time data analysis of enterprise digital operations based on big data according to claim 6, characterized in that: Dynamic feature importance screening, evaluate the feature contribution every 10 minutes, if feature X j If the importance of a feature is less than the threshold for three consecutive evaluation cycles, the feature is removed and a new significant feature is detected; Evaluate the feature importance every 10 minutes and dynamically adjust the feature set: For feature X j , its importance I j Calculate the average variance reduction brought by this feature in all trees. The calculation method is as follows: Among them, I j Represented as feature X j The importance of M is the total number of trees, T m Represented as the mth tree, n L Represents the number of samples of the current leaf node, Gain(X j ,L) is expressed as j The split gain on the leaf node L, N is represented by the total number of samples; Detect newly emerged features. If the mutual information between its single variable and the target variable exceeds the threshold, add the feature to the model and initialize the relevant splitting rules.

8. The method for real-time data analysis of enterprise digital operations based on big data according to claim 1, characterized in that: In step A4, it is assumed that the real-time data stream is associated with the historical data warehouse through a spatiotemporal alignment engine: in, It is represented as a fuzzy join operation based on the business primary key and time tolerance, where key represents the business primary key and τ represents the time tolerance. Represented as the output enhanced view, Represented as a real-time data stream, It is represented as a subset of the historical data warehouse, and Δt2 is represented as the time range offset of the historical data; The enhanced data stream after correlation adopts a hybrid strategy for dynamic KPI calculation. The calculation method of real-time aggregate indicators is as follows: in, Expressed as a real-time aggregate indicator, r i Expressed as the original indicator value, that is, the value of the i-th data point; q i It is represented as the time decay weight, C is represented as the total number of data points, and the number of original indicator values ​​involved in the calculation.

9. The method for real-time data analysis of enterprise digital operations based on big data according to claim 8, characterized in that: Calculate the outlier value for each KPI. The calculation method of the outlier value is as follows: Where Q represents the outlier, v represents the business type, v1 represents the business impact, v2 represents the trend deviation, v3 represents the data confidence, U1 represents the business impact weight, U2 represents the trend deviation weight, and U3 represents the data confidence weight; A three-level response is triggered based on the anomaly score Q. When the calculated anomaly value range is [0.5, 0.7), an email notification alarm is triggered; when the calculated anomaly value range is [0.7, 0.9), an SMS alarm is triggered; when the calculated anomaly value Q ≥ 0.9, the interface is automatically repaired.

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