Method and device for early warning and dynamic adjustment of tobacco business enterprise strategy

Abstract

The present application relates to the technical field of enterprise risk management, in particular to a tobacco business enterprise strategy risk early warning and dynamic adjustment method and device, a multi-level risk feature library and multi-dimensional business operation data are obtained, and a multi-dimensional business feature vector is generated; a target risk feature node is determined based on feature vector matching, a risk occurrence probability is output through a risk prediction model; a dynamic early warning threshold is obtained by self-adaptive updating based on historical early warning feedback data and real-time data distribution characteristics; an early warning event is triggered in response to the risk occurrence probability being greater than or equal to the dynamic early warning threshold, a root node and an influence weight are determined based on a causal network model reverse tracing; a closed-loop adjustment strategy is generated based on the influence weight and pushed to an execution terminal, realizing adaptive and accurate early warning and dynamic closed-loop adjustment of strategic risks.

Description

Technical Field

[0001] This invention relates to the field of enterprise risk management technology, and in particular to a method and equipment for risk early warning and dynamic adjustment of tobacco commercial enterprise strategies. Background Technology

[0002] With the deepening of enterprise digital transformation, strategic risk management has become a crucial component of core competitiveness. Modern enterprises typically employ multi-tiered strategic management systems, breaking down macro-strategic objectives into actionable tactical targets and business operations, forming a complex strategic execution network. In this context, how to monitor the risk situation in real time during strategic execution and quickly pinpoint the root causes and generate effective adjustment strategies when risk events occur has become a critical technical challenge that urgently needs to be addressed in the field of enterprise strategic management.

[0003] Currently, in the field of corporate strategic risk management, risk warnings are typically implemented using rule matching based on fixed thresholds or simple statistical models. However, due to the highly dynamic and complex nature of the corporate strategic execution environment, fluctuations in business data are often influenced by a combination of factors, including seasonality, market cycles, and policy changes. Existing fixed threshold mechanisms are ill-suited to such real-time fluctuations, easily leading to delayed warnings or frequent false alarms, failing to provide decision-makers with timely and accurate risk signals. Furthermore, when a risk event is triggered, current technologies largely rely on manual experience for post-event review, lacking automated reverse tracing mechanisms to accurately pinpoint the root cause and its impact weight on multi-level strategic objectives. Because of the complex hierarchical dependencies and causal relationships among corporate strategic objectives, manual review is not only inefficient but also fails to comprehensively assess the transmission effect of risk events throughout the entire strategic system, resulting in adjustment strategies that often only address the symptoms, not the root cause.

[0004] Furthermore, the existing adjustment strategy generation process lacks a verification step to ensure compatibility with core strategic objectives, potentially leading to adjustments that appear reasonable locally but deviate from the overall strategic direction at the global level. Simultaneously, effective technical support for end-to-end data security and execution feedback loops is lacking; risk warning, source tracing analysis, strategy generation, and execution tracking are fragmented, hindering the formation of a continuously optimized closed-loop management mechanism. Therefore, an intelligent technical solution capable of adaptive early warning, precise source tracing, and dynamic closed-loop adjustment is urgently needed. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a method and equipment for risk warning and dynamic adjustment of tobacco commercial enterprises' strategies, which can realize precise prevention and dynamic optimization of strategic risks based on multi-dimensional data fusion and adaptive algorithms.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: Methods for risk warning and dynamic adjustment of tobacco commercial enterprise strategies, including the following steps: Obtain a preset multi-level risk feature library, which contains multiple risk feature nodes with hierarchical relationships. Each risk feature node is associated with a corresponding baseline feature vector and an initial warning threshold. Obtain multi-dimensional business operation data of the target business system, clean and fuse the multi-dimensional business operation data, and generate multi-dimensional business feature vectors; The target risk feature node corresponding to the multidimensional business operation data is determined by matching the multidimensional business feature vector with the baseline feature vector of each risk feature node in the multi-level risk feature library. The multidimensional business feature vector is processed by a preset risk prediction model to output the probability of risk occurrence of the target risk feature node; The historical early warning feedback data and real-time data distribution characteristics of the target risk feature node are obtained. Based on the historical early warning feedback data and the real-time data distribution characteristics, the initial early warning threshold of the target risk feature node is adaptively updated through a preset parameter optimization algorithm to obtain a dynamic early warning threshold. In response to the probability of the risk occurring being greater than or equal to the dynamic warning threshold, a risk warning event is triggered for the target risk feature node, and the target risk feature node is traced back to its source based on a preset causal network model to determine the root node that triggered the risk warning event and the influence weight of the root node on multi-level target nodes. Based on the influence weights, a closed-loop adjustment strategy is generated for the multi-level target nodes, and the closed-loop adjustment strategy is pushed to the corresponding execution terminal to complete the dynamic adjustment of business risks.

[0007] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: A risk warning and dynamic adjustment device for tobacco commercial enterprise strategy includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the risk warning and dynamic adjustment method for tobacco commercial enterprise strategy described above.

[0008] The beneficial effects of this invention are as follows: The risk warning and dynamic adjustment method and device for tobacco commercial enterprise strategy of this invention constructs a multi-level risk feature library containing hierarchical relationships, and transforms multi-dimensional business operation data into multi-dimensional business feature vectors for matching with the benchmark feature vectors in the library. This achieves structured alignment between business data and strategic risk elements, overcoming the shortcomings of traditional methods in terms of coarse risk identification granularity and difficulty in covering multi-level strategic objectives. Based on this, a threshold update loss function is constructed using historical warning feedback data and real-time data distribution characteristics, and the initial warning threshold is adaptively iteratively updated using a gradient descent algorithm. This allows the warning threshold to automatically optimize with fluctuations in the business environment and historical feedback, effectively balancing the false alarm rate and the missed alarm rate, and significantly improving the accuracy and timeliness of risk warning in dynamic environments. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating the risk warning and dynamic adjustment method for tobacco commercial enterprise strategy according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the multi-level risk feature database construction process according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the workflow of the AI ​​adaptive risk warning module in an embodiment of the present invention; Figure 4 This is a schematic diagram of the closed-loop management process for strategic dynamic adjustment in accordance with an embodiment of the present invention. Figure 5 This is a schematic diagram of the risk warning and adjustment result visualization interface according to an embodiment of the present invention; Figure 6 This is a structural example diagram of a risk warning and dynamic adjustment device for tobacco commercial enterprise strategy, as described in an embodiment of the present invention. Label Explanation: 1. Risk warning and dynamic adjustment equipment for tobacco commercial enterprises' strategies; 2. Processor; 3. Memory. Detailed Implementation

[0010] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0011] Please refer to Figure 1 One embodiment of the present invention is as follows: A risk warning and dynamic adjustment method for tobacco commercial enterprises' strategies, through the construction of a multi-level risk feature database and the combination of adaptive algorithms, achieves accurate identification of business risks and dynamic optimization of strategies, including the following steps: Step S100: Obtain a preset multi-level risk feature library. The multi-level risk feature library contains multiple risk feature nodes with hierarchical relationships. Each risk feature node is associated with a corresponding baseline feature vector and an initial warning threshold.

[0012] Specifically, the multi-level risk feature database serves as a benchmark for risk identification. The hierarchical relationships reflect the decomposition logic of strategic objectives from macro to micro levels, and may include multiple levels such as strategic objectives, overall requirements, strategic tasks, and specific development paths. Each risk feature node not only represents a specific risk point (such as "logistics delivery delay risk" or "core talent loss risk"), but also digitally represents the typical manifestations of that risk through a benchmark feature vector, and is configured with an initial warning threshold as a preliminary judgment standard. This feature database is not static but can be dynamically updated in real time to synchronize with industry policies, corporate strategic adjustments, and other information to ensure the timeliness and adaptability of risk elements. The construction process of the multi-level risk feature database can be referenced. Figure 2 This structured feature library design allows abstract strategic risks to be transformed into calculable and comparable digital objects, laying the foundation for subsequent automated early warning systems.

[0013] Step S200: Obtain multi-dimensional business operation data of the target business system, clean and fuse the multi-dimensional business operation data, and generate multi-dimensional business feature vectors.

[0014] Specifically, multidimensional business operation data encompasses various internal and external information reflecting the business status. Internal data can include operational indicators, personnel data, and compliance records from business systems such as ERP and human resource management systems; external data can include relevant regulations, market environment, and industry regulatory dynamics. Since raw data often suffers from inconsistent formats, missing values, or noise interference, it requires cleaning and fusion processing. The cleaning process includes removing outliers, filling in missing values, and standardizing data formats; the fusion process involves establishing relationships between data from different sources and uniformly transforming structured and unstructured data into multidimensional business feature vectors. This process achieves the transformation from "raw data" to "machine-understandable features," enabling subsequent risk matching and prediction to be based on a comprehensive and accurate data foundation.

[0015] Step S300: Match the multi-dimensional business feature vector with the baseline feature vector of each risk feature node in the multi-level risk feature library to determine the target risk feature node corresponding to the multi-dimensional business operation data.

[0016] Specifically, the matching process essentially calculates the similarity between the current business state and each risk baseline state in a high-dimensional feature space. When the multi-dimensional business feature vector is highly similar to the baseline feature vector of a certain risk feature node, it indicates that the current business operation data contains the significant features of that risk, thus identifying that node as the target risk feature node. This feature vector-based matching method overcomes the limitations of traditional keyword matching and can capture the risk patterns hidden behind the data. For example, even if some indicator values ​​do not directly exceed the limit, but their combination pattern conforms to the feature vector distribution of a certain risk, the system can still identify them, thereby improving the sensitivity and accuracy of risk perception.

[0017] Step S400: Process the multi-dimensional business feature vector through a preset risk prediction model and output the probability of risk occurrence of the target risk feature node.

[0018] Specifically, the risk prediction model can be a machine learning model (such as random forests or neural networks) trained on historical data. This model takes a multi-dimensional business feature vector as input, comprehensively considers the contribution of each dimension to the occurrence of risk, and outputs a continuous probability value between 0 and 1, rather than a simple "yes / no" binary classification result. This probability value quantifies the actual likelihood of the risk event represented by the target risk feature node occurring under the current business conditions. By introducing probability prediction, the system can provide a refined decision-making basis for subsequent risk classification and threshold comparison, avoiding missed or incorrect judgments caused by rigid rule-based judgments.

[0019] Step S500: Obtain historical early warning feedback data and real-time data distribution characteristics of the target risk feature node. Based on the historical early warning feedback data and real-time data distribution characteristics, adaptively update the initial early warning threshold of the target risk feature node through a preset parameter optimization algorithm to obtain a dynamic early warning threshold.

[0020] Specifically, this is the core element of achieving "adaptive" early warning in this application. Traditional fixed thresholds are difficult to adapt to dynamic changes in the business environment, easily leading to frequent false alarms when data fluctuations are large, or missed alarms when risk patterns evolve. This step addresses this issue by introducing two key inputs: first, historical early warning feedback data, including past false alarm and missed alarm rates, reflecting the model's performance deviations over historical periods; second, real-time data distribution characteristics, such as data volatility variance, reflecting the stability of the current business environment. The parameter optimization algorithm uses this information to iteratively adjust the initial early warning threshold, ensuring that the resulting dynamic early warning threshold can both correct historical deviations and adapt to current real-time fluctuations. For example, when the recent false alarm rate is high, the algorithm automatically raises the threshold to reduce invalid alarms; when real-time data fluctuates drastically, the algorithm appropriately relaxes the threshold to avoid misjudging normal fluctuations as risks. This mechanism ensures that the early warning standard always dynamically adapts to the actual business situation.

[0021] Step S600: In response to the probability of risk occurrence being greater than or equal to the dynamic warning threshold, a risk warning event is triggered for the target risk feature node, and the target risk feature node is traced back to its source based on a preset causal network model to determine the root node that triggered the risk warning event and the influence weight of the root node on the multi-level target nodes.

[0022] Specifically, when the probability of a risk occurring reaches or exceeds a dynamically adjusted threshold, the system determines that the risk has been established and triggers an early warning event. Subsequently, the system does not stop at simply issuing an alarm but automatically initiates a reverse tracing mechanism. The causal network model pre-constructs the causal relationship topology between various risk elements. By treating the target risk feature node as a child node, it traverses the set of parent nodes upwards along the causal chain, and calculates the posterior probability of each parent node using conditional probability distributions, thereby accurately locating the root cause node leading to the current risk. Simultaneously, the system also assesses the influence weight of the root cause node on higher-level strategic objectives (i.e., multi-level target nodes). This process examines isolated risk events within the overall strategic framework, clarifying "who caused the risk" and "how much damage the risk inflicts on strategic objectives," providing a scientific basis for subsequently developing targeted adjustment strategies.

[0023] Step S700: Generate a closed-loop adjustment strategy for multi-level target nodes based on the influence weight, and push the closed-loop adjustment strategy to the corresponding execution terminal to complete the dynamic adjustment of business risks.

[0024] Specifically, the closed-loop adjustment strategy is an action plan tailored to the source tracing results. The system prioritizes target nodes requiring intervention according to their impact weight, and generates specific strategies that include resource allocation instructions (such as increasing budgets and adding personnel) and execution path correction instructions (such as optimizing processes and adjusting plans). These strategies are pushed to the corresponding execution terminals (such as the mobile devices or office systems of department heads) to guide frontline personnel in risk management. More importantly, the subsequent execution effect data from the execution terminals is fed back into the system as input for the next cycle of threshold updates, thus forming a complete closed loop of "monitoring-early warning-source tracing-adjustment-feedback". This closed-loop mechanism ensures that risk management is not a one-off emergency response, but a continuous and optimized long-term governance process, effectively guaranteeing the smooth achievement of strategic goals.

[0025] Through steps S100 to S700 above, this embodiment constructs a complete main process for risk warning and dynamic adjustment. This process achieves accurate risk identification through feature library and vectorized matching, solves the rigidity problem of fixed thresholds through adaptive threshold updates, achieves in-depth analysis from phenomenon to essence through causal tracing and weight evaluation, and ensures the effective implementation of adjustment measures through closed-loop strategy generation and feedback mechanisms.

[0026] Embodiment 2 of the present invention is as follows: This embodiment of the risk warning and dynamic adjustment method for tobacco commercial enterprises' strategies, building upon Embodiment 1, further details the generation process of multi-dimensional business feature vectors and the matching mechanism for target risk feature nodes. In this embodiment, the multi-level risk feature library includes first-level target nodes and second-level business indicators, with a mapping relationship between them. Specifically, this hierarchical structure decomposes abstract strategic goals into quantifiable, concrete indicators. For example, a first-level target node could be "building a high-quality workforce system," while its mapped second-level business indicators include specific observation points such as "core talent turnover rate," "employee skill matching degree," and "training coverage rate." This mapping relationship is not only the logical foundation for data organization but also a key basis for subsequent feature vector concatenation and semantic alignment, ensuring that the underlying data accurately represents the upper-level strategic intent.

[0027] In cleaning and fusing multidimensional business operation data to generate multidimensional business feature vectors, this embodiment employs a strategy of separately encoding and then fusing heterogeneous data. Specific steps include: extracting structured and unstructured data corresponding to the second-level business indicators from the multidimensional business operation data. For example, for the indicator of "core talent turnover rate," structured data may directly originate from statistical reports in the human resources system, expressed as specific percentage values; while unstructured data may come from employee satisfaction survey reports, exit interview records, or industry salary analysis documents. Since these two types of data have fundamental differences in modality, directly mixing them would lead to information loss or noise amplification; therefore, feature extraction needs to be performed separately.

[0028] Next, feature extraction is performed on both structured and unstructured data to obtain a first feature vector and a second feature vector. Specifically, for structured data, methods such as normalization, one-hot encoding, or statistical feature engineering can be used to transform it into a numerical first feature vector; for unstructured data, natural language processing techniques (such as pre-trained models like BERT and Word2Vec) can be used to extract semantic embeddings and generate a second feature vector representing the deeper meaning of the text. This separate processing approach ensures that all types of data can be expressed in their most suitable mathematical form, avoiding the unsuitability of a single encoding method for heterogeneous information.

[0029] Subsequently, based on the mapping relationship between the first-level target nodes and the second-level business indicators, the first and second feature sub-vectors are concatenated and dimensionality reduced to generate a multi-dimensional business feature vector that matches the multi-level risk feature library. This concatenation is not a simple vector chaining, but rather a semantic alignment and fusion based on the mapping relationship. For example, if multiple second-level business indicators point to the same first-level target node, the sub-vectors corresponding to these indicators will be given higher weights or specific positional encodings during concatenation to strengthen their contribution to the overall features. Dimensionality reduction (such as PCA principal component analysis or autoencoders) is used to remove redundant information and noise, compressing the high-dimensional concatenated vector into a compact and information-rich multi-dimensional business feature vector. This process not only reduces the complexity of subsequent calculations, but more importantly, it achieves a transformation from "data features" to "strategic semantic features" through dimensionality reduction, enabling the generated feature vector to truly reflect the intrinsic connection between business operation status and strategic objectives.

[0030] After obtaining the multidimensional business feature vector, it needs to be accurately matched with nodes in the database. In this embodiment, for each risk feature node in the multi-level risk feature database, the feature similarity between the multidimensional business feature vector and the baseline feature vector of the risk feature node is calculated. To quantify this similarity, this application uses cosine similarity as the metric, and its calculation formula is as follows: ; in, Indicates feature similarity. This represents the dimension of the multidimensional business feature vector and the baseline feature vector. Represents the multidimensional business feature vector at the th Feature values ​​in each dimension The baseline eigenvector is represented at the th Feature values ​​across multiple dimensions. The reason for choosing cosine similarity over Euclidean distance is that strategic risk features are often high-dimensional and sparse. We focus more on the directional consistency (i.e., the similarity of risk patterns) between the business state vector and the risk benchmark vector, rather than the absolute numerical magnitude. For example, even if a company's various indicators are generally lower than the industry average, as long as the proportional relationship between its indicators is consistent with the direction of a certain risk pattern, cosine similarity can still accurately capture that risk signal.

[0031] In specific matching decisions, risk feature nodes with a feature similarity greater than or equal to a preset matching threshold are identified as target risk feature nodes. The preset matching threshold is typically determined based on ROC curve analysis of historical validation sets, for example, set to 0.85 to achieve the best balance between recall and precision. If multiple risk feature nodes have a feature similarity greater than or equal to the preset matching threshold, the risk feature node with the highest feature similarity is selected as the target risk feature node, and the first-level target node corresponding to the target risk feature node is designated as the core-level node for the current business risk assessment. This decision-making logic reflects the risk management philosophy of "focusing on the principal contradiction." In actual business scenarios, multiple risks often occur concurrently or intertwined, but resources are always limited. By selecting the node with the highest similarity, the system can automatically identify the risk point that best matches the current business state and poses the most direct threat; locking the first-level target node above it as the core-level node indicates the strategic focus for subsequent resource allocation. For example, when both "logistics delivery delay risk" and "warehousing loss risk" trigger the threshold, but the similarity between the former and the latter is 0.92 and 0.86, the system will prioritize the former and establish "modern logistics quality improvement action" as the core level node, thereby ensuring that the adjustment strategy can concentrate on solving the most critical problems and avoid the failure of prevention and control caused by spreading efforts evenly.

[0032] Embodiment 3 of the present invention is as follows: This embodiment of the risk warning and dynamic adjustment method for tobacco commercial enterprises' strategies, based on Embodiment 1 or 2, further details the adaptive update mechanism for the warning threshold. (See also...) Figure 3Specifically, this involves acquiring historical early warning feedback data and real-time data distribution characteristics of target risk feature nodes. Based on these characteristics, a preset parameter optimization algorithm is used to adaptively update the initial early warning threshold for the target risk feature nodes, resulting in a dynamic early warning threshold. This process is crucial in transforming static risk management standards into intelligent parameters that dynamically adapt to changes in the business environment. Traditional fixed threshold methods often struggle to account for the unique characteristics of different periods. For example, they can easily generate numerous false alarms during peak seasons when data is generally high, while they may be slow to react to minor anomalies during off-seasons when data is stable. This embodiment solves these technical problems by introducing both historical feedback and real-time distribution as driving factors and using a mathematical optimization model to automatically find the optimal threshold point under the current environment.

[0033] In practical implementation, the first step is to acquire historical early warning feedback data for the target risk feature nodes within a preset historical period. This historical early warning feedback data includes historical false positive rates and historical false negative rates. The historical false positive rate reflects the frequency with which the system incorrectly identifies normal business fluctuations as risks over a past period, while the historical false negative rate reflects the frequency with which the system fails to identify genuine risk events. These two indicators directly characterize the performance deviation direction of the current early warning model. Simultaneously, it is also necessary to acquire the real-time data distribution characteristics of multi-dimensional business operation data within a preset real-time period. These real-time data distribution characteristics include data volatility variance. Data volatility variance quantifies the dispersion or instability of current business data. For example, when the external market environment fluctuates drastically or the internal system is in a transition period, data volatility variance typically increases significantly. Incorporating this characteristic allows the threshold update algorithm to perceive the "noise level" of the environment, avoiding over-adjustment of the threshold when the data itself is unstable, which could lead to model failure.

[0034] Based on the above input data, this embodiment constructs a specialized threshold update loss function to guide parameter optimization. Specifically, the threshold update loss function is constructed based on historical false positive rate, historical false negative rate, and data volatility variance. The design objective of this loss function is to minimize the overall risk cost, and its calculation formula is as follows: ; in, This indicates the value of the threshold-updated loss function. This indicates the warning threshold parameter that needs to be updated. This represents the total number of historical early warning samples within the preset historical period. Indicates the first The historical false alarm rate of a historical early warning sample Indicates the first The historical underreporting rate of a historical early warning sample and These represent the penalty weights for false positives and false negatives, respectively. Indicates the variance of the data fluctuation. Represents the regularization coefficient. This indicates the initial warning threshold.

[0035] To fully disclose the technical principles of this algorithm, the physical meaning and technical effects of each term in the formula are explained in detail below. The first two terms of the formula constitute the main error penalty terms. The first term... Penalties are specifically designed to target false alarms. (Based on historical false alarm rates.) When the value is high, in order to make the loss function Minimize, gradient descent algorithm drives the threshold to be updated Moving away from false alarm rates, i.e., tending to raise the threshold, thereby increasing the bar for triggering alerts and reducing invalid alarms. Conversely, the second item... Penalize false negatives. Based on the historical false negative rate... At higher levels, the algorithm will drive Reduce parameters to improve system sensitivity and prevent real risks from being missed. and The settings reflect a business-oriented flexibility. For example, for risks related to "production safety," the consequences of underreporting are far greater than those of false alarms, therefore, settings can be configured... This forces the algorithm to prioritize a low false negative rate when making trade-offs; and for risks related to "routine administrative approvals," to avoid frequently disturbing staff, settings can be configured... Prioritizing the control of false alarms. This configurable penalty weight mechanism allows the same algorithm to adapt to the differentiated risk control preferences in different business scenarios.

[0036] The third term of the formula This is an adaptive regularization term, a key innovation that distinguishes this application from conventional optimization algorithms. Its function is to adjust the regularization based on the variance of real-time data fluctuations. Dynamic adjustment of the initial threshold The degree of dependence. When the variance of real-time data fluctuates. When the variance is relatively small, it indicates a stable business environment and high data reliability. In this case, the constraint of the regularization term is weak, and the algorithm mainly relies on the historical feedback of the first two terms to significantly adjust the threshold in pursuit of ultimate accuracy. However, when the variance of real-time data fluctuates... A significant increase in the coefficient of the regularization term indicates that the current data contains a large amount of random noise or sudden anomalies, and historical feedback may be temporarily invalid or misleading. In this case, the coefficient of the regularization term automatically increases, providing strong constraints. Do not deviate from the initial threshold. Too far. This is equivalent to adding a "stabilizer" to the system when the environment is chaotic, preventing the threshold from fluctuating drastically or overfitting due to short-term noise, and ensuring the robustness of the early warning system under extreme conditions.

[0037] After constructing the loss function, the threshold update loss function is iteratively solved using the gradient descent algorithm. The goal is to minimize the output value of the threshold update loss function, thereby updating the parameters of the initial warning threshold for the target risk feature node to obtain the dynamic warning threshold. Specifically, the system calculates the loss function... about The gradient is calculated and updated incrementally along the negative gradient direction according to a preset learning rate. The value of . To ensure the algorithm can respond promptly without excessively consuming computational resources, this embodiment sets a clear convergence condition: when iterating through the gradient descent algorithm, the iteration terminates when the difference between the threshold update loss function values ​​of two adjacent iterations is less than a preset convergence threshold, and the currently obtained value is set. The value serves as a dynamic early warning threshold. For example, the preset convergence threshold can be set to... When the change in loss value calculated in two consecutive iterations is less than this value, it indicates that the algorithm has converged to a local optimum or a global optimum. This represents the optimal balance point under the combined influence of current historical feedback and real-time fluctuations.

[0038] Through the aforementioned mechanism, this embodiment achieves a leap from "human experience-based setting" to "data-driven adaptive" early warning thresholds. Compared to fixed thresholds, this solution can not only automatically correct deviations based on historical performance but also dynamically adjust its aggressiveness or conservatism according to the stability of the real-time environment. This dual adaptive capability effectively solves the industry problem of risk warnings being "either inaccurate or overly frequent" in complex and ever-changing business environments, providing a reliable data foundation for the accurate triggering of subsequent risk warning events.

[0039] Embodiment four of the present invention is as follows: This embodiment of the risk warning and dynamic adjustment method for tobacco commercial enterprises, building upon any of the aforementioned embodiments, further details the root cause tracing mechanism of risk warning events and the quantitative assessment method for their impact weights. Specifically, based on a pre-defined causal network model, the target risk characteristic nodes are traced back to determine the root cause node that triggers the risk warning event and the impact weight of the root cause node on multi-level target nodes. This process aims to solve the technical problem in traditional risk management of "knowing only the surface of the risk but not the underlying cause," replacing subjective experience-based judgment with mathematical causal reasoning, and providing an objective basis for subsequent precise resource allocation.

[0040] In its implementation, the target risk feature node is first treated as the target child node in the causal network model. Then, the model is used to traverse backwards through the set of parent nodes that have a causal relationship with the target child node. The causal network model pre-constructs a directed acyclic graph structure between business elements, where nodes represent various business indicators or risk elements, and directed edges represent causal transmission relationships. The backward traversal process is essentially tracing back from the result to the cause along the causal link.

[0041] Next, based on the conditional probability distribution among nodes in the causal network model, the posterior probability of each parent node in the set of parent nodes triggering a risk warning event in the target child node is calculated. The conditional probability distribution is typically stored in the form of a conditional probability table, representing the probability of a child node exhibiting anomalies under different parent node states. The posterior probability is calculated by combining currently observed business data evidence and using Bayesian inference or other probabilistic reasoning algorithms. This value quantifies the likelihood that each potential cause is actually the true root cause, given that the current risk has already occurred. Compared to simple correlation analysis, posterior probability can effectively eliminate spurious correlation interference and accurately pinpoint the true risk-causing factor. Subsequently, parent nodes with posterior probabilities greater than or equal to a preset source tracing threshold are identified as the root cause nodes triggering the risk warning event. The preset source tracing threshold can be set according to the business's requirements for attribution accuracy, for example, set to 0.6, to ensure that the identified root cause nodes have sufficient explanatory power.

[0042] After identifying the root cause node, it is necessary to further quantify its impact on the overall strategic objective. In this embodiment, based on the root cause node's topological hierarchy and posterior probability in the causal network model, the influence weight of the root cause node on each level of the multi-level target nodes is evaluated. To ensure the evaluation results are both scientific and computable, this application introduces an improved analytic hierarchy process (AHP). Specific steps include constructing an AHP judgment matrix containing multi-level target nodes, target risk characteristic nodes, and the root cause node. Unlike the traditional AHP method that relies on subjective expert scoring, the judgment matrix element values ​​in this embodiment are automatically generated based on objective data. The calculation rule is: the element value equals the posterior probability of the root cause node multiplied by the reciprocal of the path length from the root cause node to the multi-level target nodes. This design contains profound technical logic: the posterior probability reflects the strength of the causal relationship, i.e., "whether it is the primary cause"; while the reciprocal of the path length reflects the efficiency and tightness of risk transmission. A shorter path means less attenuation during risk transmission and a more direct impact on higher-level targets. Multiplying the two together takes into account both the credibility of the attribution and the structural characteristics of risk transmission, thus achieving an objective mapping from "probabilistic reasoning" to "decision weights".

[0043] After constructing the judgment matrix, the maximum eigenvalue and its corresponding eigenvector of the analytic hierarchy process (AHP) judgment matrix are calculated, and the eigenvectors are normalized to obtain the initial weights of the root nodes to each level of the multi-level target nodes. Normalization ensures that the sum of the weights of all root nodes is 1, facilitating subsequent horizontal comparisons and resource allocation. To ensure the logical consistency of the judgment matrix and avoid weight distortion due to data noise or model bias, this embodiment also introduces a strict consistency verification mechanism. Specifically, the AHP judgment matrix undergoes a consistency check, and a consistency ratio index (CR) is calculated. The consistency ratio index reflects the degree to which the matrix deviates from complete consistency. If the consistency ratio index is less than or equal to a preset consistency threshold (usually 0.1), the judgment matrix passes the check, and the initial weights are used as the final influence weights. If the CR exceeds the threshold, it indicates a logical contradiction in the current causal inference or path calculation, and the system will automatically trigger a correction mechanism, such as adjusting the source tracing threshold or recalibrating the conditional probability table, until the consistency requirements are met. This step significantly enhances the credibility and robustness of the technical solution, ensuring that the output weight values ​​are logically rigorous and executable.

[0044] Ultimately, a closed-loop adjustment strategy is generated for multi-level target nodes based on their impact weights. Specifically, multi-level target nodes are sorted in descending order of impact weight, and a closed-loop adjustment strategy containing resource allocation instructions and execution path correction instructions is generated based on the sorting results. This weight-based sorting mechanism reflects the risk management philosophy of "focusing on the principal contradiction." The root cause node with the highest weight represents the key point that poses the greatest threat to strategic objectives and requires the most urgent intervention. The system will prioritize matching it with resource allocation instructions (such as increasing budgets, supplementing manpower, upgrading equipment, etc.) and execution path correction instructions (such as optimizing processes, adjusting plans, revising systems, etc.). For example, if the source analysis shows that the impact weight of "insufficient salary competitiveness" on "building a high-quality team system" is 0.6, far higher than the 0.3 of "inadequate training system," then the generated adjustment strategy will prioritize recommending an increase in salary levels, rather than blindly increasing the number of training sessions. This data-driven prioritization effectively avoids the inefficient, pepper-sprinkling of resources, ensuring that limited management resources are accurately directed to the key links that have the greatest leverage effect on achieving strategic objectives, thereby truly achieving precise prevention and dynamic optimization of strategic risks.

[0045] For reference Figure 4 Embodiment five of the present invention is as follows: Building upon Example 4, this method for risk warning and dynamic adjustment of tobacco commercial enterprise strategies, after generating a closed-loop adjustment strategy, includes a compatibility verification step before pushing the closed-loop adjustment strategy to the corresponding execution terminal to prevent the strategy from deviating from the core strategic direction or resulting in superficial solutions. This mechanism constitutes a key line of defense for the system's intrinsic security closed loop, unlike the open-loop recommendation model in existing technologies that only focuses on risk elimination while neglecting strategic consistency, ensuring that every dynamic adjustment is carried out within the established strategic framework.

[0046] Specifically, adaptability verification first requires quantifying the degree of matching between the strategy and the objective. The system extracts the strategy feature vector of the closed-loop adjustment strategy and the benchmark target feature vector of the multi-level target nodes. The strategy feature vector is the result of multi-dimensional digital representation of the generated adjustment plan, and its dimensions can cover resource input intensity, expected effectiveness period, risk coverage breadth, compliance index, and cross-departmental collaboration. The benchmark target feature vector is the standard profile of the multi-level target nodes under ideal conditions. For example, the benchmark vector corresponding to "building a high-quality team system" may include inherent attributes such as talent structure balance, innovation capability index, and long-term development sustainability. Through vectorization, the abstract strategic adaptability problem is transformed into a geometric computation problem in high-dimensional space, providing a mathematical foundation for objective evaluation.

[0047] Subsequently, the strategy fit similarity between the strategy feature vector and the benchmark target feature vector is calculated. The calculation logic for this similarity is as follows: the Euclidean distance between the strategy feature vector and the benchmark target feature vector in the multidimensional feature space is calculated, and this Euclidean distance is mapped to a preset similarity interval to obtain the strategy fit similarity. The Euclidean distance intuitively reflects the absolute numerical difference between two vectors; the smaller the distance, the closer the strategy is to the benchmark. To facilitate unified judgment, the Sigmoid function or a linear normalization function is typically used to map the Euclidean distance in the non-negative real number domain to the similarity interval [0,1]. For example, when the Euclidean distance is 0, the similarity is 1, indicating a perfect fit; as the distance increases, the similarity monotonically decreases and approaches 0. This mapping method makes the fit comparable across different business scenarios and also facilitates the subsequent setting of a unified verification threshold.

[0048] Based on the calculated similarity, the system provides tiered responses. If the strategy adaptation similarity is greater than or equal to a preset verification threshold, the closed-loop adjustment strategy is deemed to have passed the adaptation verification, allowing it to be pushed to the corresponding execution terminal. The preset verification threshold can be flexibly configured according to business fault tolerance. For example, for adjustment strategies involving significant resource allocation, the threshold can be set to 0.9 to ensure high consistency; while for routine operational fine-tuning strategies, the threshold can be appropriately relaxed to 0.75 to maintain flexibility.

[0049] More importantly, in response to a strategy adaptation similarity score below a preset verification threshold, the closed-loop adjustment strategy is deemed to have failed the adaptation verification, triggering a strategy correction mechanism. This mechanism does not simply reject the strategy; instead, it fine-tunes the parameters of resource allocation instructions and execution path correction instructions in the closed-loop adjustment strategy based on the difference dimension between the strategy feature vector and the baseline target feature vector, until the recalculated strategy adaptation similarity score is greater than or equal to the preset verification threshold. Specifically, the system identifies the main contributing dimensions that cause excessive Euclidean distance, i.e., the difference dimensions. For example, if the difference is mainly concentrated in the "long-term sustainability" dimension, it indicates that the current strategy may be overly focused on short-term risk elimination, harming long-term interests. The system will automatically reduce the weight of short-term investments in the resource allocation instructions and increase the link regarding the construction of long-term mechanisms in the execution path correction instructions. If the difference is concentrated in the "compliance" dimension, it will automatically strengthen the compliance review nodes in the path correction instructions. This fine-tuning is an automated iterative process. The system will gradually adjust the instruction parameters within a preset parameter search space, recalculating the similarity score with each adjustment, until the verification conditions are met.

[0050] Through the aforementioned closed-loop mechanism of "generation-verification-correction," this embodiment effectively solves the strategy drift problem that easily occurs in traditional risk response. Even if the initially generated adjustment strategy performs well in local optima, if it has a hidden conflict with the overall strategic objective, it can be promptly captured by the adaptability verification and corrected through self-correction. This not only ensures that the adjustment strategy always anchors to the core strategic objective, but also improves the security and effectiveness of strategy implementation, avoiding the risk of strategic execution being distorted due to blindly putting out fires. It should be understood that although this embodiment uses Euclidean distance as a similarity metric, in other embodiments, Mahalanobis distance, cosine similarity, or other metrics that can characterize the closeness between vectors can also be used, as long as they can achieve quantitative comparison between strategy and objective and drive automatic correction, they all fall within the protection scope of this application.

[0051] Embodiment six of the present invention is as follows: This embodiment, building upon embodiments one through five, further details the system's end-to-end security control mechanism and the feedback data update mechanism (step S800) for risk warning and dynamic adjustment of tobacco commercial enterprise strategies. The aim of this embodiment is to construct a closed-loop system that is both secure and reliable, and possesses continuous evolution capabilities, ensuring compliance in the risk control process and continuous optimization of model performance over time.

[0052] Including the following steps: Step S801 involves encrypting the multi-dimensional business operation data and closed-loop adjustment strategies, and transmitting the data between the target business system and the execution terminal via a secure transmission protocol. Specifically, data security is the bottom line for the operation of the strategic risk management system. This application employs a dual encryption protection system at the transport and storage layers. During data transmission, the system enforces the use of the SSL / TLS secure transmission protocol, establishing an encrypted communication tunnel between the target business system and the execution terminal to prevent sensitive content such as payroll data and risk warning information from being eavesdropped on or tampered with during network transmission. During data storage, the multi-dimensional business operation data, closed-loop adjustment strategies, and audit logs stored on disk are all encrypted using the AES-256 high-strength encryption algorithm. Even if the physical storage medium is lost or the database is illegally compromised, attackers cannot recover the plaintext data. This end-to-end encryption protection mechanism effectively meets the stringent compliance requirements of tobacco commercial enterprises for core operational data and strategic information, ensuring the system's operational security in complex network environments.

[0053] Step S802: Based on a preset role-based access control (RBAC) model, differentiated data access permissions are assigned to access terminals with different identifiers, targeting multi-level risk feature databases, risk warning events, and closed-loop adjustment strategies. Specifically, the system incorporates a role-based access control (RBAC) model, categorizing roles into strategic risk management, business execution, audit supervision, and management levels based on business functions. Access terminals with different roles are assigned strictly differentiated data views and operational permissions. For example, terminals corresponding to business execution roles can only view and receive risk warning events and related adjustment strategies within their department's scope of responsibility, and cannot access data from other departments or the global strategic feature database; audit supervision roles have read-only global log query permissions for compliance review but cannot modify any configurations; while management terminals have the highest permissions, allowing them to view system-wide risk information, approve major closed-loop adjustment strategies, and adjust global parameters. This fine-grained permission isolation mechanism follows the principle of least privilege, ensuring that each position has the necessary information to perform its duties efficiently while technically preventing unauthorized internal operations or the unauthorized dissemination of sensitive information, thus ensuring the standardization and seriousness of the risk management process.

[0054] Step S803 involves collecting execution effect data generated by the execution terminal after implementing the closed-loop adjustment strategy, and using this data as new historical early warning feedback data to update the historical early warning feedback data for adaptive updates of the dynamic early warning threshold in the next cycle. Specifically, this is a key step in achieving long-term adaptive evolution of the system and a data link that transforms the threshold update loss function L(θ) in Example 3 from a theoretical model into actual productivity. When the execution terminal implements the closed-loop adjustment strategy (such as implementing salary adjustments or replacing logistics vehicles), the system automatically tracks and collects the corresponding execution effect data. For example, after implementing an adjustment strategy for "core talent loss risk," the system continuously monitors the actual talent loss rate changes in subsequent months; after implementing an adjustment strategy for "logistics delivery delay risk," new on-time delivery rate indicators are collected. These raw execution effect data, after being cleaned, outlier removed, and timestamped, are structured and injected into the historical early warning feedback dataset. When the next early warning cycle begins, the parameter optimization algorithm in Example 3 will automatically read the dataset containing the latest intervention results, recalculate the historical false positive rate and historical false negative rate, and then drive a new round of iterative solution for the threshold update loss function L(θ). This means that if the adjustment strategy in the previous round effectively reduced the probability of risk occurrence, the system will "learn" this positive effect through feedback data and automatically fine-tune the threshold in the next round to maintain sensitivity; conversely, if the adjustment is ineffective or even leads to the deterioration of indicators, the system will also correct the threshold direction in a timely manner through a negative feedback mechanism. This "execution-feedback-learning-optimization" data feedback mechanism makes the risk early warning system no longer a static set of rules, but an intelligent agent that can continuously accumulate experience and evolve itself with the enterprise's business practices, fundamentally solving the industry pain point that traditional risk control systems peak upon launch and then gradually become ineffective.

[0055] Embodiment seven of the present invention is as follows: To illustrate the application value of the technical solution of this application in actual business, this embodiment takes the risk prevention and control scenario of a tobacco commercial enterprise adapting to a certain development strategy as an example for detailed explanation.

[0056] In this application scenario, the target business system of the system is the comprehensive management platform of a provincial tobacco commercial enterprise. The multi-level risk feature library specifically maps the core elements of a certain development strategy, namely, a four-level risk architecture that includes "one strategic goal", "one overall requirement", "four strategic tasks" and "two development paths" (including five quality improvement actions and six empowerment projects).

[0057] In this application scenario, the multi-dimensional data acquisition and fusion module connects in real-time to the enterprise's internal human resource management system, ERP system, and external industry regulatory platform. For example, when monitoring the first-level target node of "high-quality workforce system," the system collected data showing that a city-level company's core talent turnover rate reached 8% in the past three months, and that employee satisfaction survey text analysis showed that its salary competitiveness was 12% lower than the industry average. After cleaning and vectorization, the generated multi-dimensional business feature vectors were matched with the baseline feature vector of the "core talent turnover risk" node in the feature library. The calculated feature similarity was 0.92, exceeding the preset matching threshold of 0.85, thus identifying it as a target risk feature node.

[0058] For this target risk feature node, the AI ​​adaptive risk warning module demonstrated the practical effect of dynamic threshold adjustment. The initial maximum risk warning range was set at 5% to 10% churn rate. However, historical warning feedback data obtained by the system showed that talent turnover in the industry had generally increased in the past six months due to seasonal factors. Maintaining the original threshold might generate a large number of false alarms with non-substantive risks. Simultaneously, real-time data distribution characteristics showed that the current data fluctuation variance σ² was within the normal range. Based on the threshold update loss function L(θ) described in Example 3, the system, under the guidance of the false alarm penalty weight α, iteratively solved the problem using a gradient descent algorithm, automatically and adaptively adjusting the maximum risk warning range for this node from 5%-10% to 6%-11%. Since the current 8% churn rate still falls within the adjusted range, and the probability of risk occurrence predicted by the model is 0.78, higher than the dynamic warning threshold of 0.65, the system ultimately triggered a yellow warning event. This process vividly demonstrates how the algorithm can effectively filter environmental noise interference while ensuring risk sensitivity.

[0059] Upon triggering the warning, the risk tracing and impact assessment module immediately initiates a reverse tracing mechanism. Based on a pre-built causal network model, the system traverses upwards along the "core talent loss" node, calculating the posterior probability of "insufficient salary competitiveness" as 0.75 and the posterior probability of "limited career development space" as 0.65, both exceeding the preset tracing threshold of 0.6, thus identifying them as root cause nodes. Subsequently, the system constructs a hierarchical analysis judgment matrix, quantifying the impact weights of these two root cause nodes on the "high-quality team system" as 0.6 and 0.3, respectively, and on the "talent empowerment project" as 0.4 and 0.5, respectively. Based on this weight ranking, the strategic dynamic adjustment module generates resource allocation instructions including "raising the core talent salary level to 1%-5% above the industry average" and execution path correction instructions for "establishing a dual-track promotion mechanism of management and technology." Before the push, the compatibility verification mechanism calculates the Euclidean distance between the feature vector of the strategy and the feature vector of the benchmark target of the "Talent Empowerment Project". The strategy compatibility similarity obtained by mapping is 0.93, which is higher than the preset verification threshold of 0.85. It is confirmed as a valid strategy and pushed to the human resources department execution terminal.

[0060] As another parallel case study, regarding the risk of logistics delivery delays in the "Modern Logistics Quality Improvement Action," the system monitored a logistics delivery delay rate of 3% in a certain region. Although this absolute value is not high, the historical average delay rate for this region is only 1.2%, and the real-time data fluctuation variance has increased significantly, indicating an abnormal disturbance. The dynamic threshold adjustment unit accordingly tightened the warning threshold, causing a 3% delay rate to trigger a significant risk warning. Source tracing analysis identified the root causes as "aging logistics vehicles" and "unreasonable delivery route planning," with a combined weight of 0.8 in their impact on the "high-quality development system." The system then generated adjustment strategies of "replacing 10 aging logistics vehicles" and "replanning delivery routes based on big data," which were pushed to the logistics management department after passing adaptability verification.

[0061] After the above adjustment strategy is implemented on the terminal, the end-to-end security management module continuously tracks the implementation effect. For example... Figure 5As shown, the visualization module for early warning and adjustment results intuitively presents the dynamic closed loop of risk management. In the risk heat map, users can clearly see the yellow warning markers in the "Modern Logistics Quality Improvement Action" area of ​​City A. Clicking on them allows users to view details of the 3.2% delay rate and the associated root causes. In the early warning trend chart, the line graph shows the rise and fall of the number of risk warnings for the two types of risk warnings—the Quality Improvement Action and the Empowerment Project—over the past six months, reflecting the trend of a gradual decline after intervention following the concentrated outbreak of logistics risks in March. In the progress chart of the adjustment plan execution, the bar chart updates the completion status of various tasks in real time. For example, the completion rate of "Core Talent Salary Adjustment" has reached 100%, and the completion rate of "Logistics Vehicle Replacement" is 80%. When the execution terminal reports a new dropout rate of 3% and a delay rate of 1.5%, this execution effect data is automatically fed back to the historical early warning feedback dataset, driving the iterative optimization of the threshold update loss function in the next cycle. This achieves a closed-loop management of the entire chain from data perception, intelligent early warning, precise traceability, strategy generation to effect feedback, effectively ensuring the smooth implementation of the development strategy.

[0062] Please refer to Figure 6 Embodiment 8 of the present invention is as follows: The risk warning and dynamic adjustment device 1 for tobacco commercial enterprise strategy includes a processor 2, a memory 3, and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, it implements the steps in the risk warning and dynamic adjustment method for tobacco commercial enterprise strategy described in any of the above embodiments one to seven.

[0063] This invention constructs a multi-level risk feature library containing hierarchical relationships and transforms multi-dimensional business operation data into multi-dimensional business feature vectors, matching them with benchmark feature vectors in the library. This achieves structured alignment between business data and strategic risk elements, overcoming the shortcomings of traditional methods that suffer from coarse-grained risk identification and difficulty in covering multi-level strategic objectives. Furthermore, it constructs a threshold update loss function using historical early warning feedback data and real-time data distribution characteristics, and adaptively iterates and updates the initial early warning threshold using a gradient descent algorithm. This allows the early warning threshold to automatically optimize based on fluctuations in the business environment and historical feedback, effectively balancing false positives and false negatives, and significantly improving the accuracy and timeliness of risk warnings in dynamic environments.

[0064] Furthermore, after a risk warning event is triggered, this invention performs a reverse traversal based on a causal network model and calculates the posterior probability. It then constructs a judgment matrix using the analytic hierarchy process (AHP) to quantify the influence weight of the root cause node on multi-level target nodes. This tracing mechanism based on topological structure and probabilistic reasoning can accurately extract the root cause of the risk and its transmission path from complex business data, avoiding the subjectivity and bias of manual judgment and providing a quantifiable decision-making basis for subsequent strategy generation.

[0065] Furthermore, this invention introduces an adaptability verification mechanism based on Euclidean distance of feature vectors before pushing out closed-loop adjustment strategies. By calculating the similarity between the strategy feature vector and the baseline target feature vector, it ensures that the generated resource allocation instructions and execution path correction instructions are always consistent with the core strategic objectives. For strategies that fail verification, parameter fine-tuning is automatically triggered, forming an endogenous optimization closed loop of "generation-verification-correction," preventing risk response measures from deviating from the strategic main line. Combined with a full-process security control and execution effect data feedback and update mechanism, it realizes intelligent management of the entire lifecycle from data collection, risk warning, strategy generation to execution feedback, ensuring the continuous evolution and security compliance of the strategic risk control system.

[0066] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for risk early warning and dynamic adjustment of tobacco commercial enterprise strategy, characterized in that, Including the following steps: Obtain a preset multi-level risk feature library, which contains multiple risk feature nodes with hierarchical relationships. Each risk feature node is associated with a corresponding baseline feature vector and an initial warning threshold. Obtain multi-dimensional business operation data of the target business system, clean and fuse the multi-dimensional business operation data, and generate multi-dimensional business feature vectors; The target risk feature node corresponding to the multidimensional business operation data is determined by matching the multidimensional business feature vector with the baseline feature vector of each risk feature node in the multi-level risk feature library. The multidimensional business feature vector is processed by a preset risk prediction model to output the probability of risk occurrence of the target risk feature node; The historical early warning feedback data and real-time data distribution characteristics of the target risk feature node are obtained. Based on the historical early warning feedback data and the real-time data distribution characteristics, the initial early warning threshold of the target risk feature node is adaptively updated through a preset parameter optimization algorithm to obtain a dynamic early warning threshold. In response to the probability of the risk occurring being greater than or equal to the dynamic warning threshold, a risk warning event is triggered for the target risk feature node, and the target risk feature node is traced back to its source based on a preset causal network model to determine the root node that triggered the risk warning event and the influence weight of the root node on multi-level target nodes. Based on the influence weights, a closed-loop adjustment strategy is generated for the multi-level target nodes, and the closed-loop adjustment strategy is pushed to the corresponding execution terminal to complete the dynamic adjustment of business risks.

2. The method for risk warning and dynamic adjustment of tobacco commercial enterprise strategy according to claim 1, characterized in that, The multi-level risk feature library includes a first-level target node and a second-level business indicator, and there is a mapping relationship between the first-level target node and the second-level business indicator; The step of cleaning and fusing the multidimensional business operation data to generate a multidimensional business feature vector includes: Extract structured and unstructured data corresponding to the second-level business indicators from the multi-dimensional business operation data; Feature extraction is performed on the structured data and the unstructured data respectively to obtain a first feature vector and a second feature vector; Based on the mapping relationship between the first-level target node and the second-level business indicator, the first feature sub-vector and the second feature sub-vector are concatenated and dimensionality reduced to generate the multi-dimensional business feature vector that matches the multi-level risk feature library.

3. The method for risk warning and dynamic adjustment of tobacco commercial enterprise strategy according to claim 2, characterized in that, The step of matching the multi-dimensional business feature vector with the baseline feature vector of each risk feature node in the multi-level risk feature library to determine the target risk feature node corresponding to the multi-dimensional business operation data includes: For each risk feature node in the multi-level risk feature library, calculate the feature similarity between the multi-dimensional business feature vector and the baseline feature vector of the risk feature node; The formula for calculating the feature similarity is: ； in, Indicates the feature similarity, This represents the dimension of the multidimensional business feature vector and the baseline feature vector. The multidimensional business feature vector represents the first... Feature values ​​in each dimension The reference feature vector is represented at the th Feature values ​​in each dimension; The risk feature nodes whose feature similarity is greater than or equal to a preset matching threshold are identified as the target risk feature nodes; If multiple risk feature nodes have a feature similarity greater than or equal to the preset matching threshold, then the risk feature node with the largest feature similarity is selected as the target risk feature node, and the first-level target node corresponding to the target risk feature node is selected as the core-level node for the current business risk assessment.

4. The method for risk warning and dynamic adjustment of tobacco commercial enterprise strategy according to claim 1, characterized in that, The process involves acquiring historical early warning feedback data and real-time data distribution characteristics of the target risk feature node, and based on these data, adaptively updating the initial early warning threshold of the target risk feature node using a preset parameter optimization algorithm to obtain a dynamic early warning threshold, including: Acquire historical early warning feedback data of the target risk feature node within a preset historical period, wherein the historical early warning feedback data includes historical false alarm rate and historical false alarm rate; Obtain the real-time data distribution characteristics of the multi-dimensional business operation data within a preset real-time period, wherein the real-time data distribution characteristics include data fluctuation variance; A threshold update loss function is constructed based on the historical false positive rate, the historical false negative rate, and the data fluctuation variance. The threshold update loss function is iteratively solved using the gradient descent algorithm to minimize its output value. The initial warning threshold of the target risk feature node is then updated to obtain the dynamic warning threshold.

5. The method for risk warning and dynamic adjustment of tobacco commercial enterprise strategy according to claim 4, characterized in that, The step of constructing a threshold update loss function based on the historical false positive rate, the historical false negative rate, and the data volatility variance includes: The formula for calculating the threshold update loss function is as follows: ； in, This represents the value of the threshold update loss function. This indicates the warning threshold parameter that needs to be updated. This represents the total number of historical early warning samples within the preset historical period. Indicates the first The historical false alarm rate of a historical early warning sample Indicates the first The historical underreporting rate of a historical early warning sample and These represent the penalty weights for false positives and false negatives, respectively. This represents the variance of the data fluctuation. Represents the regularization coefficient. This represents the initial warning threshold; When iteratively solving the problem using the gradient descent algorithm, the iteration terminates when the difference between the values ​​of the threshold update loss function in two adjacent iterations is less than a preset convergence threshold, and the currently obtained result is updated. The value is used as the dynamic early warning threshold.

6. The method for risk warning and dynamic adjustment of tobacco commercial enterprise strategy according to claim 1, characterized in that, The method of reverse tracing the target risk feature nodes based on a preset causal network model to determine the root cause node that triggered the risk warning event and the influence weight of the root cause node on multi-level target nodes includes: The target risk feature node is taken as the target child node in the causal network model, and the set of parent nodes that have causal relationship with the target child node is traversed in reverse in the causal network model. Based on the conditional probability distribution among nodes in the causal network model, calculate the posterior probability that each parent node in the set of parent nodes will trigger the target child node to generate the risk warning event. The parent node whose posterior probability is greater than or equal to a preset tracing threshold is determined as the root source node that triggered the risk warning event; Based on the root node's topological level in the causal network model and the posterior probability, the influence weight of the root node on each level of the multi-level target nodes is evaluated.

7. The method for risk warning and dynamic adjustment of tobacco commercial enterprise strategy according to claim 6, characterized in that, The evaluation of the influence weights of the root source node on each level of the multi-level target nodes, based on the root source node's topological hierarchy in the causal network model and the posterior probability, includes: Construct a hierarchical analysis and judgment matrix that includes the multi-level target nodes, the target risk feature nodes, and the root cause nodes; The element values ​​in the hierarchical analysis judgment matrix are determined based on the posterior probability of the root node and the path length from the root node to the multi-level target node. Calculate the maximum eigenvalue and its corresponding eigenvector of the hierarchical analysis judgment matrix, and normalize the eigenvector to obtain the initial weight of the root node to each level node in the multi-level target node. Perform a consistency check on the hierarchical analysis judgment matrix and calculate the consistency ratio index; In response to the consistency ratio index being less than or equal to a preset consistency threshold, the initial weight is used as the influence weight; The generation of a closed-loop adjustment strategy for the multi-level target nodes based on the influence weights includes: The multi-level target nodes are sorted in descending order of their influence weights, and a closed-loop adjustment strategy containing resource allocation instructions and execution path correction instructions is generated based on the sorting results.

8. The method for risk warning and dynamic adjustment of tobacco commercial enterprise strategy according to claim 7, characterized in that, Before pushing the closed-loop adjustment strategy to the corresponding execution terminal, the method further includes a step of performing an adaptability verification on the closed-loop adjustment strategy, the adaptability verification step including: Extract the strategy feature vector of the closed-loop adjustment strategy, and extract the baseline target feature vector of the multi-level target node; Calculate the policy adaptation similarity between the policy feature vector and the baseline target feature vector; The calculation logic for the strategy adaptation similarity is as follows: calculate the Euclidean distance between the strategy feature vector and the benchmark target feature vector in the multidimensional feature space, and map the Euclidean distance to a preset similarity interval to obtain the strategy adaptation similarity. In response to the strategy adaptation similarity being greater than or equal to a preset verification threshold, the closed-loop adjustment strategy is determined to have passed the adaptation verification, and the closed-loop adjustment strategy is allowed to be pushed to the corresponding execution terminal. In response to the strategy adaptation similarity being less than the preset verification threshold, it is determined that the closed-loop adjustment strategy has failed the adaptation verification, triggering a strategy correction mechanism. Based on the difference dimension between the strategy feature vector and the benchmark target feature vector, the parameters of the resource allocation instruction and execution path correction instruction in the closed-loop adjustment strategy are fine-tuned until the recalculated strategy adaptation similarity is greater than or equal to the preset verification threshold.

9. The method for risk warning and dynamic adjustment of tobacco commercial enterprise strategy according to claim 1, characterized in that, It also includes full-process security control and execution feedback steps: The multi-dimensional business operation data and the closed-loop adjustment strategy are encrypted and transmitted between the target business system and the execution terminal through a secure transmission protocol. Based on a preset role-based access control model, differentiated data access permissions are assigned to access terminals with different identifiers for the multi-level risk feature database, the risk warning events, and the closed-loop adjustment strategy. The execution effect data generated by the execution terminal after executing the closed-loop adjustment strategy is collected, and the execution effect data is used as new historical early warning feedback data to update the historical early warning feedback data for adaptive updating of the dynamic early warning threshold in the next cycle.

10. A risk warning and dynamic adjustment device for tobacco commercial enterprise strategy, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps in the risk warning and dynamic adjustment method for tobacco commercial enterprise strategy as described in any one of claims 1-9.

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