Data processing method of full-stack AI enterprise management system

Abstract

The invention discloses a data processing method of a full-stack AI enterprise management system. The method comprises the following steps: constructing a time difference sequence of financial links; a delay fluctuation value is calculated through multi-scale wavelet transform and time sequence analysis, and an abnormal service link is identified by using an isolated forest algorithm; generating a consistency deviation index fusing a financial field matching rate and a domain rule weight based on a sequence-to-sequence model of an attention mechanism; inputting the deviation index and the time difference sequence into a space-time diagram convolutional network, and tracing a root business link; a timestamp synchronization strategy is adjusted according to the tracing path, and a financial rule engine is triggered to generate a correction entry; an LSTM model and a reinforcement learning agent are introduced, so that optimization verification and parameter adaptive adjustment are realized; and finally generating an abnormal positioning report and a risk assessment result. By means of the technical scheme, automatic detection, source tracing, correction and continuous optimization are carried out on abnormities in the enterprise management process, and the reliability, compliance and system autonomy ability of financial data circulation are improved.

Description

Technical Field

[0001] This application relates to the field of computer data processing technology, and specifically discloses a data processing method for a full-stack AI enterprise management system. Background Technology

[0002] With the deep penetration of artificial intelligence technology into enterprise management, full-stack AI systems are gradually becoming the core infrastructure supporting intelligent enterprise operations. Modern enterprise management systems need to integrate multi-dimensional business data such as finance, inventory, and human resources, and achieve efficient and accurate data flow and decision support in complex and dynamic environments. Especially in financial data processing scenarios, the entire process from collection, verification to transformation must maintain high consistency and timeliness to ensure the reliability of key functions such as loan balancing, report generation, and risk warning. However, the data processing mechanisms of existing enterprise management systems generally lack the ability to fine-tune the time dimension, making it difficult to cope with time differences caused by system heterogeneity, load fluctuations, or network latency during multi-stage collaboration, thus restricting the improvement of anomaly tracing efficiency and system self-healing capabilities.

[0003] The timestamp offsets and processing delays that occur when financial data flows across different stages constitute a hidden bottleneck affecting data consistency. Traditional methods typically focus only on the verification logic of the data content itself, neglecting the dynamic evolution of the time-series relationships between various business nodes. When anomalies such as loan imbalances occur, operations personnel often have to manually backtrack multiple isolated log segments, unable to automatically link the causal chain between time differences and data deviations. Furthermore, existing technologies lack the ability to intelligently identify latency fluctuation patterns; they cannot dynamically set anomaly thresholds based on historical behavior, nor can they quantify the degree of inconsistency in conjunction with business rules. This results in long problem localization cycles and high correction costs, severely weakening the enterprise's responsiveness in high-frequency trading or real-time risk control scenarios.

[0004] Therefore, there is an urgent need for a full-stack AI data processing method that can deeply integrate time series analysis, unsupervised anomaly detection, graph structure dependency modeling and adaptive correction mechanisms to achieve accurate identification, root cause tracing and automatic repair of time-difference driven business anomalies in enterprise management processes. Summary of the Invention

[0005] This invention provides a data processing method for a full-stack AI enterprise management system, which can effectively solve the technical problem mentioned in the background art that existing enterprise management systems lack the ability to finely model the time dimension, making it difficult to identify data inconsistencies caused by timestamp offsets and processing delays during the cross-process flow of financial data, and thus unable to achieve automatic tracing and self-healing of abnormal states.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A data processing method for a full-stack AI enterprise management system includes the following specific steps:

[0008] Step S1, Data Acquisition and Time Series Construction: Obtain the start and end timestamps of each business link from the enterprise's financial data flow path, and construct a time difference sequence covering the complete financial link from data acquisition to transformation and processing;

[0009] Step S2, Delay Fluctuation Analysis and Anomaly Identification: The time difference sequence is processed using time series analysis methods to calculate the delay fluctuation value of each business link, and anomaly business links with delay fluctuation values ​​exceeding dynamic thresholds are identified based on unsupervised learning algorithms.

[0010] Step S3, Data Consistency Comparison Step: For the abnormal business process, compare the transformed financial data with the financial data collected and verified to generate a consistency deviation index that integrates the financial field matching rate and the domain rule weight.

[0011] Step S4, root cause business process tracing step: Input the consistency deviation index and the time difference sequence into the graph-based anomaly detection model. By analyzing the dependencies between business processes, output the problem tracing path for locating the root cause business process causing delay fluctuations.

[0012] Step S5, Financial Self-Balancing Correction Step: Based on the problem tracing path, extract the loan transaction records affected by time differences from the financial data, generate an imbalance point correction sequence by adjusting the timestamp synchronization strategy, and trigger the financial rule engine to automatically generate corrective entries;

[0013] Step S6, Optimization Verification and Feedback Step: Based on the imbalance point correction sequence, recalculate the delay fluctuation value of the overall financial data flow, and introduce an adaptive learning mechanism to dynamically adjust the data processing parameters in order to verify the optimization effect and obtain the optimized time difference sequence;

[0014] Step S7, Report Generation and Risk Assessment: Based on the optimized time difference sequence, generate an anomaly location report, and calculate the response capability improvement indicators and business risk reduction confirmation results for each business link based on the preset risk quantification model.

[0015] Preferably, in the delay fluctuation analysis and anomaly identification steps, the time series analysis method is a multi-scale wavelet transform method, specifically including wavelet multi-resolution analysis of the time difference sequence to decompose it into fluctuation components of different frequency bands. The Daubechies wavelet basis function is used for four-level decomposition, corresponding to high-frequency noise, short-term fluctuations, medium-term disturbances, and long-term trend components, respectively. The instantaneous amplitude and phase information of each component are extracted through Hilbert transform, and the energy entropy of each fluctuation component is further calculated. The energy entropy is defined as the Shannon entropy measure of the signal energy distribution within each frequency band; a larger value indicates that the energy of the time delay in that stage is more dispersed and the fluctuation is more unstable. The energy entropy is used as the delay fluctuation value, thereby achieving sensitive capture of local abrupt changes in non-stationary time series.

[0016] Preferably, the identification of abnormal business processes based on unsupervised learning algorithms specifically employs the Isolation Forest algorithm. During the training phase, multiple random binary trees are constructed using a sample set of latency fluctuation values ​​under historical normal operating conditions. Each tree forms isolated paths by recursively and randomly partitioning the feature space. During the testing phase, the average path length of the latency fluctuation values ​​of each business process is calculated. The shorter the path length, the easier it is for the sample to be isolated, and the higher the probability of it being an anomaly. The dynamic threshold is dynamically generated based on the historical distribution of the latency fluctuation values ​​through statistical learning. Specifically, latency fluctuation values ​​over the past 30 days are collected through a sliding time window, and the absolute deviation between the interquartile range and the median is calculated. After removing historical extreme value interference using the Grubbs test, the dynamic threshold is set to Q3 plus 1.5 times the IQR to ensure that the threshold adaptively drifts with the system's operating status.

[0017] Preferably, in the data consistency comparison step, the consistency deviation index is generated specifically through a sequence-to-sequence model based on an attention mechanism. The encoder part of this model adopts a bidirectional long short-term memory network structure, receiving the field sequences of the original collected data and the transformed data as input. The decoder part adopts a unidirectional LSTM structure to gradually generate field matching scores. The attention weight matrix is ​​calculated by the dot product of the decoder's hidden state and the encoder's output at each time step, so that the model focuses on key financial fields such as account codes, amounts, and direction indicators during the comparison process. At the same time, the loan balance rule is transformed into a constraint loss term and embedded in the model training process. This loss term forces that the square of the difference between the total debit amount and the total credit amount of all transaction records be less than or equal to 0.01 yuan. Finally, the consistency deviation index is a linear combination of the weighted field matching rate and the degree of rule violation, and the weight coefficients are pre-configured according to the financial importance level.

[0018] Preferably, the graph-based anomaly detection model is a spatiotemporal graph convolutional network, in which each business link is abstracted as a node in the graph. The node feature vector includes the latency fluctuation value, data throughput, number of error retries, and the most recent consistency deviation index of the link. The data flow relationship and temporal dependency relationship between links are abstracted as directed edges in the graph. The edge weight is determined by the call frequency of upstream and downstream services and the average transmission time. Spatial feature extraction adopts graph convolution operation to aggregate the information of neighboring nodes to update the representation of the central node. Temporal feature extraction adopts stacked one-dimensional causal convolutional layers to capture the evolution pattern of node state in continuous time steps. The model output is an anomaly score heatmap for each node. High-score areas correspond to the root business links most likely to cause cascading delays. The problem tracing path is jointly determined by the backpropagation gradient path and the maximum impact edge sequence.

[0019] Preferably, in the financial self-balancing correction step, the adjusted timestamp synchronization strategy is implemented using a Bayesian optimization algorithm. This algorithm maintains a Gaussian process surrogate model to fit the black-box functional relationship between the timestamp alignment scheme and the overall latency fluctuation value. In each iteration, the next candidate alignment scheme is selected through the expected improvement criterion. Clock offset compensation factors are dynamically injected into the microservice cluster to minimize the overall financial data flow latency fluctuation value as the objective function. The optimal timestamp alignment scheme is searched. The objective function comprehensively considers the weighted average latency fluctuation value of the backbone link and the branch link. The search space is limited to ±50 milliseconds of the local clock of each service relative to the global coordination time, with a step size accuracy of 0.1 milliseconds, ensuring accurate synchronization without affecting system stability.

[0020] Preferably, in the optimization verification and feedback step, the recalculation of the delay fluctuation value of the overall financial data flow is performed using a Long Short-Term Memory (LSTM) network model. The LSTM model has a two-layer hidden structure, with each layer containing 128 memory units. The input sequence is the delay fluctuation value of each link within a continuous 72 time windows, and the output is the overall fluctuation prediction value at the next moment. The model is pre-trained to predict the delay fluctuation range under normal operating conditions. The training data comes from the historical monitoring logs during the steady-state operation of the system. The labels are generated by aligning real abnormal event sequences labeled by experts. If the prediction error exceeds 3 times the standard deviation, it is judged as a residual anomaly, triggering a new round of correction cycle.

[0021] Preferably, the adaptive learning mechanism is a reinforcement learning agent, which interacts with the environment and obtains a reward signal based on whether the recalculated latency fluctuation value is lower than a preset threshold. The reward function is designed as a negative standardized fluctuation value increment plus a pulse positive reward for a successful repair event. The state space consists of the latency, load, and consistency deviation of each business link at present. The action space is defined as an adjustable combination of parameters, including message queue priority, cache refresh cycle, and batch processing window size. The agent adopts a deep Q network architecture, the experience replay pool has a capacity of 100,000 transition samples, the target network update cycle is every 1,000 steps, and new strategies are explored through an ε greedy strategy. Based on this, the strategy parameters for data processing are iteratively updated to achieve closed-loop autonomous optimization.

[0022] Preferably, in the data acquisition and time series construction steps, the time difference series is collected through a distributed link tracing system deployed in a microservice architecture. This system is based on open telemetry standards, inserts probe modules at each service entry and exit point, and automatically assigns a unique tracking ID and timestamp label to the financial data flowing through each service. The timestamp acquisition accuracy reaches the nanosecond level, and the clocks of each node are synchronized using the NTP protocol, with the deviation controlled within ±5 microseconds. The link data is aggregated to the central time series database through an asynchronous streaming pipeline, supporting the rapid retrieval of complete call chains by tracking ID, service name, time interval, and other dimensions, ensuring the integrity and traceability of the time difference series.

[0023] Compared with existing technologies, this invention has the following advantages: By constructing a time difference sequence covering the entire lifecycle of financial data, this invention, for the first time, incorporates the dynamic fluctuation characteristics of the time dimension into the consistency assurance system of an enterprise management system, breaking through the technical limitations of traditional methods that only focus on static data content verification; it uses a method combining multi-scale wavelet transform and energy entropy to quantify delay fluctuations, effectively separating noise from real system disturbances, and improving the sensitivity and robustness of anomaly detection; it combines the isolated forest algorithm to achieve unsupervised anomaly identification, avoiding dependence on a large number of labeled samples and adapting to the actual scenario of frequent changes in enterprise business models; and it achieves semantic-level data consistency deviation indicators by generating a sequence-to-sequence model enhanced by an attention mechanism and fusing domain rules. Instead of simple field matching, the solution employs comparison; it introduces spatiotemporal graph convolutional networks for root cause tracing, fully leveraging the topological dependencies between business processes to significantly improve the accuracy of problem localization; based on a Bayesian-optimized timestamp synchronization strategy and a reinforcement learning-driven adaptive feedback mechanism, a complete closed-loop governance system from detection, localization, correction to verification is formed, which not only reduces the cost of manual intervention but also enables the system to continuously evolve; the overall solution deeply integrates artificial intelligence with enterprise financial management logic, realizing automated and intelligent processing of implicit time-difference-driven anomalies in enterprise management processes, significantly shortening fault response time, enhancing the reliability and compliance of financial data flow, and providing a new technical path for the stability and autonomy of enterprise-level AI systems. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the overall technical solution architecture of the data processing method for the full-stack AI enterprise management system proposed in this invention;

[0025] Figure 2 This is a schematic diagram of the core principle framework of anomaly identification and root cause tracing based on spatiotemporal graph convolutional networks and multi-scale temporal analysis in this invention; Detailed implementation method

[0026] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0028] Currently, in the cross-process flow of enterprise financial data, timestamp offsets and processing delays caused by system heterogeneity, load fluctuations, or network latency constitute hidden bottlenecks affecting data consistency. Traditional methods typically focus only on the verification logic of the data content itself, neglecting the dynamic evolution characteristics of the time-series relationships between various business nodes, resulting in low efficiency in anomaly tracing and a lack of self-healing capabilities. To address the above technical problems, this invention proposes a full-stack AI data processing method that deeply integrates time-series analysis, unsupervised anomaly detection, graph structure dependency modeling, and adaptive correction mechanisms, and applies it to the data processing method of a full-stack AI enterprise management system.

[0029] refer to Figure 1 The overall technical architecture of the data processing method for the full-stack AI enterprise management system described in this invention includes a distributed tracing layer, a time-series analysis engine, a consistency comparison module, a graph structure anomaly detection model, a financial self-balancing correction unit, an optimization verification feedback loop, and a risk assessment report generator. These modules work collaboratively to achieve end-to-end closed-loop governance from raw timestamp collection to final risk quantification output. The following will elaborate on the seven core steps of this invention.

[0030] In the data processing method of the aforementioned full-stack AI enterprise management system, step S1, namely the data acquisition and time series construction step, aims to obtain a high-precision, traceable financial data flow time difference sequence. Specifically, this step is implemented through a distributed tracing system deployed in a microservice architecture. This system is built on open telemetry standards, inserting lightweight probe modules at each service entry and exit point to automatically assign a unique tracking ID and timestamp label to the financial data flowing through each service. The timestamp acquisition accuracy reaches the nanosecond level, using the NTP protocol to synchronize the local clocks of each computing node, ensuring that the global clock deviation is controlled within ±5 microseconds. Specifically, Java Agent bytecode enhancement technology is used to embed the probe modules, and the timestamp label format is [Tracking ID][UTC Timestamp][Service Name], where the UTC timestamp is synchronized from the time server via the NTP protocol, and the deviation calibration algorithm uses the Marzullo algorithm. When a financial transaction is initiated from the acquisition service and flows sequentially through the verification service, conversion service, and accounting service, each probe records the precise time when the data packet enters and leaves the current service, respectively recorded as the start timestamp. With end timestamp For the first Each business process, its processing delay Defined as All stages Arranged chronologically, they form a complete time difference sequence. ,in This represents the number of business processes involved in the financial chain. This sequence is aggregated in real-time to a central time-series database via asynchronous streaming pipelines, supporting millisecond-level retrieval by tracking ID, service name, time interval, etc., ensuring the completeness and timeliness of the data required for subsequent analysis. See [link to process details] for more information. Figure 1 The "Distributed Link Tracing Layer" part.

[0031] In the data processing method of the aforementioned full-stack AI enterprise management system, step S2, namely the delay fluctuation analysis and anomaly identification step, aims to extract discriminative fluctuation features from the time difference sequence and automatically identify abnormal links that deviate from the normal behavior pattern. Specifically, this step first uses a multi-scale wavelet transform method to perform wavelet multi-resolution analysis on the time difference sequence. The Daubechies wavelet basis function (db4) is selected to perform a four-level decomposition on the original sequence, obtaining four fluctuation components of different frequency bands: the first level corresponds to the high-frequency noise component, reflecting instantaneous jitter; the second level corresponds to the short-term fluctuation component, characterizing minute-level disturbances; the third level corresponds to the medium-term disturbance component, reflecting hour-level changes; and the fourth level and the approximation coefficients together constitute the long-term trend component, characterizing day-level slow drift. Subsequently, a Hilbert transform is applied to the detail coefficients of each level to extract their instantaneous amplitude. With phase ,in Indicates the number of decomposition layers. Calculates the signal energy within each frequency band based on the instantaneous amplitude. And further define energy entropy as follows:

[0032]

[0033] This formula shows that energy entropy It is a Shannon entropy measure of energy distribution within each frequency band. A larger value indicates more dispersed energy and more unstable time delay fluctuations within that band. The energy entropy of the most sensitive frequency band (usually the second or third layer) is calculated. As the delay fluctuation value of this business process Subsequently, the Isolation Forest algorithm was used to analyze all stages. Unsupervised anomaly identification is performed. During the training phase, 100 random binary trees are constructed using a sample set of latency fluctuation values ​​collected during the system's steady-state operation over the past 90 days. Each tree forms isolated paths by recursively and randomly partitioning the feature space. Specifically, the partitioning rule uses axial parallel random cutting, randomly selecting feature dimensions and randomly generating cutting points. The maximum depth of a single tree is limited to 5. During the testing phase, the average path length of latency fluctuation values ​​for each business process is calculated. The shorter the path length, the more likely the sample is to be isolated, and the higher the probability of it being an anomaly. The anomaly threshold is not a fixed value, but is dynamically generated through a sliding time window: the window length is set to 30 days, updated daily, calculating the interquartile range (IQR = Q3 - Q1) and median absolute deviation (MAD) of the delayed fluctuation values ​​within the window, and combining this with the Grubbs test to remove historical extreme values, ultimately setting the dynamic threshold as follows: If a certain link If the dynamic threshold is exceeded, the process is marked as an abnormal business process and proceeds to the next step.

[0034] In the data processing method of the aforementioned full-stack AI enterprise management system, step S3, namely the data consistency comparison step, aims to quantify the degree of semantic inconsistency between financial data before and after the processing of abnormal business processes. Specifically, this step records the processed financial data for the abnormal business processes marked in step S2. Original financial data records during data collection and verification Deep alignment is performed. The alignment process is executed by an attention-based sequence-to-sequence model. The encoder of this model employs a bidirectional long short-term memory (BiLSTM) network structure, receiving... and The input consists of a sequence of fields (such as account code, amount, direction indicator, summary, auxiliary accounting items, etc.), each field is embedded as a 128-dimensional vector, and the sequence length is uniformly padded to 64. The encoder outputs a context-aware hidden state sequence. The decoder uses a unidirectional LSTM structure to progressively generate a matching score for each field. The attention weight matrix is ​​calculated by normalizing the dot product of the decoder's current hidden state and the encoder's output at each time step using softmax, allowing the model to dynamically focus on key financial fields during the comparison process, such as automatically increasing the weight of "amount" and "direction indicator". In addition, the loan balancing rule is transformed into a constraint loss term embedded in the model training process, which forces all transaction records to meet the constraint. Yuan. Specifically, the constraint loss term is constructed using the Lagrange multiplier method: ,in, For the total loss function, Here, we take the regularization coefficient. , This is the sum of the amounts of all debit accounts predicted by the model for a single sample. This is the sum of the amounts predicted by the model for all credit accounts for the same sample. The model is trained on 100,000 labeled samples using the Adam optimizer (learning rate 0.001) until the validation set loss converges. The model's final output is the weighted field matching rate M and the rule violation degree R. The consistency deviation index C is defined as:

[0035]

[0036] in, and For pre-configured weighting coefficients based on financial importance levels, such as for core general ledger accounts, For auxiliary detailed items, This indicator This comprehensively reflects the dual impact of data content bias and rule violation.

[0037] In the data processing method of the aforementioned full-stack AI enterprise management system, step S4, namely the root cause business process tracing step, aims to locate the root cause of latency fluctuations and data inconsistencies. Specifically, this step compares the consistency deviation index C generated in step S3 with the time difference sequence constructed in step S1. The inputs are shared into a graph-based anomaly detection model. (Reference) Figure 2 The model is a Spatiotemporal Graph Convolutional Network (ST-GCN). First, a business dependency graph is constructed: each business process is abstracted as a node in the graph. Node feature vectors It includes four attributes—delay fluctuation value. Data throughput (number of records processed per unit time), number of error retries, and most recent consistency deviation index. The data flow relationships and temporal dependencies between stages are abstracted into directed edges. edge weight The spatial feature extraction is determined by the product of the frequency of upstream and downstream service calls and the average transmission time, reflecting the strength of the dependency. Graph convolution operations are used for each node. Aggregate information from its first-order neighbor nodes and update the representation of the central node. ,in Gather for the neighbors, The normalization constant is For spatial convolution kernels, The ReLU activation function is used. Temporal feature extraction employs three stacked one-dimensional causal convolutional layers with a kernel size of 3 and a stride of 1, ensuring that only historical information is used to predict the current state, capturing the evolution patterns of node features over continuous time steps. The model ultimately outputs an anomaly score heatmap for each node; the higher the score, the more likely the node is to be the root cause of cascading delays. The problem tracing path is jointly determined by two parts: first, by calculating the gradient of the input features with respect to the output score through backpropagation, identifying the most influential nodes, and selecting the top 3 nodes by absolute value; second, by backtracking along dependency paths with edge weights ≥ 0.8, selecting the node with the largest gradient as the root cause. This path is the problem tracing path used to locate the root cause business process.

[0038] In the data processing method of the aforementioned full-stack AI enterprise management system, step S5, namely the financial self-balancing correction step, aims to automatically correct the lending imbalance caused by time differences. Specifically, this step extracts the set of lending transaction records affected by time differences from the central financial database based on the problem tracing path output in step S4. These records typically exhibit a misalignment of timestamps, resulting in incorrect pairing of debit and credit records within the same accounting period. The core of the correction lies in adjusting the timestamp synchronization strategy. This strategy employs a Bayesian optimization algorithm: the algorithm maintains a Gaussian process surrogate model to fit the time alignment scheme. Overall delay fluctuation value Black-box functional relationship between In each iteration, the next candidate alignment scheme is selected based on the expected improvement criterion. And dynamically inject clock skew compensation factors into the microservice cluster. (for the first) (one service), to minimize the objective function The objective function comprehensively considers the weighted average latency fluctuation of the backbone and branch links, with weights set according to business importance. The search space is limited to ±50 milliseconds of each service's local clock relative to global coordination time (UTC), with a step size accuracy of 0.1 milliseconds. After a maximum of 20 iterations, the optimal alignment scheme is obtained. Based on this, an imbalance point correction sequence is generated. Subsequently, the financial rules engine is triggered to re-pair debit and credit records based on the corrected timestamps and automatically generate corrective entries. For example, if a debit record is delayed and recorded on the next day, while a credit record has already been recorded on the same day, a red-ink reversal entry and a blue-ink repeat entry are generated to ensure debit and credit balance.

[0039] In the data processing method of the aforementioned full-stack AI enterprise management system, step S6, namely the optimization verification and feedback step, aims to verify the correction effect in a closed loop and continuously optimize system parameters. Specifically, this step first re-executes steps S1 to S2 based on the imbalance point correction sequence generated in step S5, calculating the delayed fluctuation value of the overall financial data flow. This calculation is not a simple repetition; instead, it uses a pre-trained Long Short-Term Memory (LSTM) network model for prediction validation. This LSTM model has a two-layer hidden structure, each containing 128 memory units. The input is a sequence of delay fluctuation values ​​for each stage within 72 consecutive time windows (each window is 5 minutes), and the output is the overall fluctuation prediction for the next time step. The model was trained using historical monitoring logs from steady-state operation before the system went live, and the labels were generated by aligning expert-annotated sequences of real abnormal events. If the actual observed values... With the predicted value If the residual exceeds 3 times the historical standard deviation, it is determined that there is a residual anomaly, triggering a new round of correction cycle. At the same time, a reinforcement learning agent is introduced as an adaptive learning mechanism. The state space s of this agent consists of the current delays, CPU loads, memory occupancies, and consistency deviation metrics of each business process; the action space is defined as a combination of adjustable system parameters, including message queue priority (taking values from 1 to 5), cache refresh period (taking values from 1 to 60 seconds), and batch processing window size (taking values from 100 to 10,000 records). The reward function is designed as , where is the increment of the standardized fluctuation value, is an indicator variable for successful repair events (1 for success, otherwise 0), . The agent adopts a deep Q-network (DQN) architecture, and the capacity of the experience replay pool is 100,000 transfer samples , the target network is softly updated every 1000 steps, and the exploration strategy adopts an ε-greedy strategy (initial ε = 0.9, decaying 0.01 every 1000 steps). By interacting with the environment, the agent continuously iteratively updates the Q-network parameters and outputs the optimal action , dynamically adjusts the data processing parameters, and realizes closed-loop autonomous optimization.

[0040] In the data processing method of the above full-stack AI enterprise management system, the step S7, that is, the report generation and risk assessment step, aims to provide interpretable decision support and quantify risk returns. Specifically, this step automatically generates a structured anomaly location report based on the optimized time difference sequence obtained in step S6. The report content includes: anomaly occurrence time, involved business processes, delay fluctuation value, consistency deviation metrics, visual graph of the problem tracing path (see Figure 2 ), details of correction measures, list of corrective entries. At the same time, based on a preset risk quantification model, two core indicators are calculated: one is the response ability improvement indicator of each business process, defined as the percentage decrease in the average processing delay before and after correction; the other is the confirmation result of business risk reduction, evaluating the reduction amplitude of the probability of loan imbalance events occurring within the next 7 days after correction through Monte Carlo simulation. For example, if the response ability improvement indicator of a payment settlement process is 42% and the confirmation result of business risk reduction is "high confidence (p < 0.01)", it indicates that the correction measure is significantly effective. This report is pushed to the operation and maintenance dashboard and the management mailbox, providing data basis for continuous improvement.

[0041] To further illustrate the practical application effect of this invention, a specific application scenario is constructed: During the "Double 11" promotion, a large e-commerce platform experienced intermittent loan and borrowing imbalances in its order financial system. Traditional methods require manual log checks, taking more than 4 hours. After applying the method of this invention, the system completes the entire process within 5 minutes: Step S1 captures the time difference sequences of four links—payment gateway, order service, inventory service, and financial core—through distributed tracing; Step S2 identifies an abnormal latency fluctuation value in the inventory service (energy entropy reaches 2.8, far exceeding the dynamic threshold of 1.9); Step S3 compares and finds that the amount matching rate between inventory deduction records and financial accounting records is only 85%; Step S4 uses the ST-GCN model to trace the root cause to network jitter between the inventory service and the message queue; Step S5 Bayesian optimization adjusts the clock compensation factor of the inventory service to +12.3 milliseconds, generating 3 corrective entries; Step S6 LSTM model verifies that the fluctuation value has returned to normal, and reinforcement learning agent raises the message queue priority to level 4; Step S6 generates a report confirming a 92% reduction in risk. The entire process requires no human intervention, ensuring strong consistency of financial data during the promotional period.

[0042] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A data processing method of a full-stack AI enterprise management system, characterized by, The method comprises the following steps: Step S1, obtaining the start timestamp and end timestamp of each business link from the enterprise financial data flow path, and constructing a time difference sequence covering the complete financial link from data collection to conversion processing; Step S2, processing the time difference sequence by using a time series analysis method, calculating the delay fluctuation value of each business link, and identifying abnormal business links whose delay fluctuation values exceed a dynamic threshold based on an unsupervised learning algorithm; Step S3, for the abnormal business link, comparing the converted financial data with the financial data at the time of collection and verification to generate a consistency deviation index that integrates the financial field matching rate and the field rule weight; Step S4, inputting the consistency deviation index and the time difference sequence into an anomaly detection model based on a graph structure, outputting a problem tracing path for locating the root business link causing the delay fluctuation by analyzing the dependency relationship between business links; Step S5, according to the problem tracing path, extracting the loan and debit transaction records affected by the time difference from the financial data, generating an unbalance point correction sequence by adjusting the timestamp synchronization strategy, and triggering the financial rule engine to automatically generate correction entries; Step S6, based on the unbalance point correction sequence, recalculating the delay fluctuation value of the overall financial data flow, and introducing an adaptive learning mechanism to dynamically adjust the data processing parameters to verify the optimization effect and obtain the optimized time difference sequence; Step S7, according to the optimized time difference sequence, generating an abnormal positioning report, and calculating the response capacity improvement index and the business risk reduction confirmation result of each business link based on a preset risk quantification model.

2. The data processing method of the full-stack AI enterprise management system according to claim 1, characterized in that: In the delay fluctuation analysis and anomaly identification step, the time series analysis method is a multi-scale wavelet transform method, which performs 4-layer wavelet decomposition on the time difference sequence, uses Daubechies wavelet basis function to obtain high-frequency noise, short-term fluctuation, medium-term disturbance and long-term trend components, and extracts the instantaneous amplitude of each component by Hilbert transform, and calculates the Shannon entropy of the energy distribution of each frequency band signal as the delay fluctuation value.

3. The data processing method of the full-stack AI enterprise management system according to claim 2, characterized in that: The unsupervised learning algorithm used in the abnormal business link identification step is an isolation forest algorithm, which uses a sample set of delay fluctuation values under historical normal working conditions to construct multiple random binary trees, and determines the abnormality by calculating the average path length of the current delay fluctuation value in the test stage, and dynamically sets the threshold as Q3 plus 1.5 times IQR based on the delay fluctuation value in the past 30 days in the sliding time window.

4. The data processing method of the full-stack AI enterprise management system according to claim 1, characterized in that: In the data consistency comparison step, the consistency deviation index is generated by a sequence-to-sequence model based on an attention mechanism, the encoder of the model uses a bidirectional long short-term memory network, the decoder uses a unidirectional LSTM structure, the attention weight is calculated by the dot product of the decoder hidden state and the encoder output, and the balance rule of borrowing and lending is converted into a constraint loss term embedded in the training process, forcing the square of the difference between the total amount of the borrower and the total amount of the lender to be less than or equal to 0.01 yuan, and the final consistency deviation index is a linear combination of the weighted field matching rate and the rule violation degree.

5. The data processing method of the full-stack AI enterprise management system according to claim 1, characterized in that: The graph structure-based anomaly detection model is a spatio-temporal graph convolution network, which abstracts each business link as a node, and the node feature vector contains delay fluctuation value, data throughput, error retry count and consistency deviation index, abstracts the data flow conversion and time sequence dependence between links as a directed edge, and the edge weight is determined by the service call frequency and the average transmission time consumption, the spatial features aggregate neighbor information through graph convolution operation, and the time features are extracted through one-dimensional causal convolution layer stacking, the model outputs an abnormal score heat map, and the problem tracing path is determined by the joint determination of the back propagation gradient path and the maximum impact edge sequence.

6. The data processing method of the full-stack AI enterprise management system according to claim 1, characterized in that: In the financial self-balancing correction step, the adjustment timestamp synchronization strategy adopts a Bayesian optimization algorithm, maintains a Gaussian process proxy model to fit the black box function relationship between timestamp alignment scheme and overall delay fluctuation value, selects candidate schemes through the expected improvement criterion, injects clock offset compensation factors in the microservice cluster, the target function integrates the weighted delay fluctuation mean of the main link and the branch link, the search space is limited to the ±50 millisecond range of each service local clock relative to the global coordinated time, and the step accuracy is 0.1 millisecond.

7. The data processing method of the full-stack AI enterprise management system according to claim 1, characterized in that: In the optimization verification and feedback step, the delay fluctuation value of the overall financial data flow is recalculated using a long short-term memory network model with two layers of hidden structures, each layer contains 128 memory units, the input is the delay fluctuation value of each link in the continuous 72 time windows, and the output is the overall fluctuation prediction value at the next moment. If the prediction error exceeds 3 times the standard deviation, it is determined as residual anomaly and triggers a new round of correction cycle.

8. The data processing method of the full-stack AI enterprise management system according to claim 1, characterized in that: The adaptive learning mechanism is a reinforcement learning agent, the state space is composed of the delay, load and consistency deviation of each business link, the action space includes message queue priority, cache refresh period and batch processing window size, the reward function is the negative standardized fluctuation value increment plus the pulse positive reward of successful repair events, the agent uses a deep Q network architecture, the experience replay pool capacity is 100,000 transition samples, the target network is updated every 1000 steps, and the data processing parameters are explored and iteratively updated through the epsilon-greedy strategy.

9. The data processing method of the full-stack AI enterprise management system according to claim 1, characterized in that: In the data collection and time series construction step, the time difference sequence is collected through a distributed link tracking system deployed in the microservice architecture. The system is based on the open telemetry standard, inserts probe modules at each service entrance and exit, tags financial data with a unique tracking ID and nanosecond-level timestamp, synchronizes node clocks using the NTP protocol, controls the deviation within ±5 microseconds, and aggregates link data through an asynchronous streaming pipeline to a central time series database.

10. The data processing method of the full-stack AI enterprise management system according to claim 4, characterized in that: In the sequence-to-sequence model, the field sequence is embedded as a 128-dimensional vector, the sequence length is uniformly padded to 64, the key financial fields include subject code, amount, direction identifier, abstract and auxiliary accounting items, and the weight coefficients are pre-configured according to the financial importance level. The field matching rate weight of the core general ledger subject is set to 0.7, the rule violation weight is set to 0.3, and the weights of both auxiliary detail subjects are set to 0.5.

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