Reinforcement learning-based high-value enterprise gradient cultivation policy precise delivery and effect evaluation method and system

Abstract

The present application belongs to the technical field of precise delivery and effect evaluation, and particularly relates to a high-value enterprise gradient cultivation policy precise delivery and effect evaluation method and system based on reinforcement learning, which classifies and processes multi-source heterogeneous data of enterprises and extracts features, respectively uses corresponding feature learning algorithms to complete vectorization representation for structured time series data, unstructured text data and graph data, and obtains unified low-dimensional basic state features through attention fusion mode; a hierarchical composite state space is built to distinguish continuous change feature dimensions and discrete level dimensions, set state jump control logic, and adaptively adjust feature weights combined with the development level of the enterprise; internal dynamic factors and external dynamic factors of the system are split and integrated into state expression, real-time monitoring of data distribution deviation is performed, and human abnormal data interference on feature expression is identified and inhibited.

Description

Technical Field

[0001] This invention belongs to the field of precision targeting and effect evaluation technology, and in particular relates to a method and system for precision targeting and effect evaluation of high-value enterprise gradient cultivation policies based on reinforcement learning. Background Technology

[0002] Currently, enterprise tiered development efforts largely rely on manual experience combined with traditional statistical analysis methods for policy implementation and effectiveness evaluation, resulting in insufficient data utilization and one-sided characterization. Existing solutions mostly rely solely on explicit indicators self-reported by enterprises, failing to uncover implicit aspects such as technological potential and growth momentum, and struggling to effectively identify instances of enterprises deliberately embellishing or inflating data to obtain policy support. Furthermore, traditional models lack dynamic adaptability in the face of internal and external environmental changes such as iterative industrial policies, macroeconomic fluctuations, and industry restructuring. Data distribution drift and sudden environmental changes can easily lead to distorted characterization, making it difficult to guarantee the accuracy of policy implementation and the reliability of evaluation results.

[0003] On the other hand, existing technologies mostly employ fixed-parameter models for analysis, failing to establish differentiated evaluation logic for different enterprise cultivation levels. They also lack effective means to cover extreme working conditions and rare scenarios, and models trained solely on historical data have insufficient generalization ability. Furthermore, most systems lack a closed-loop iterative optimization mechanism based on policy implementation effects. The models cannot continuously correct their inference logic based on actual cultivation results, and problems such as hidden state inference bias and unreasonable feature weights persist, making it difficult to meet the actual business needs of accurate identification of high-value enterprises, dynamic optimization of tiered cultivation policies, and long-term evaluation in complex scenarios. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for precise deployment and effectiveness evaluation of high-value enterprise tiered cultivation policies based on reinforcement learning, the method comprising: The system classifies and extracts features from multi-source heterogeneous data of enterprises. Corresponding feature learning algorithms are used to complete vectorized representation of structured time series data, unstructured text data, and graph data, and unified low-dimensional basic state features are obtained through attention fusion. A hierarchical composite state space is constructed, distinguishing between continuously changing feature dimensions and discrete level dimensions, setting state transition control logic, and adaptively adjusting feature weights based on the enterprise's development level. The system separates internal and external dynamic factors and integrates them into state representation, monitors data distribution shifts in real time, and identifies and suppresses interference from human-caused abnormal data on feature representation. We adopt a continuous learning and multi-model switching strategy to adapt to dynamic changes in the environment, and at the same time build a digital twin simulation environment to complete model pre-training under multiple working conditions based on the simulation scenario. A probabilistic inference model is established for the unobservable latent variables of the system. The distribution of the latent state is solved by combining time series observation data, and the uncertainty of the latent state inference is quantified. By combining three methods—information supplementation, confidence constraints, and knowledge transfer from similar samples—the observation data and latent state representation are improved, and subsequent decision-making behavior is constrained based on the inference confidence. Using the policy development cycle as a node, the implicit state inference model is corrected in reverse by using data on the implementation effect, forming a closed-loop iterative optimization mechanism; By connecting the data links of all modules and integrating various features to obtain the global state, and dynamically scheduling each functional module according to the operating conditions, the features are output to the reinforcement learning module to realize policy decision-making and effect evaluation.

[0005] Furthermore, embodiments of the present invention also provide a system for precise targeting and effectiveness evaluation of high-value enterprise tiered cultivation policies based on reinforcement learning, characterized by comprising: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the aforementioned method for precise targeting and effectiveness evaluation of high-value enterprise gradient cultivation policies based on reinforcement learning by executing the machine-executable instructions.

[0006] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of a computer device reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the computer device to execute the above-mentioned method for precise targeting and effect evaluation of high-value enterprise gradient cultivation policies based on reinforcement learning.

[0007] Based on the above, through multi-source heterogeneous data fusion and hierarchical composite state space construction, combined with temporal modeling and probabilistic inference techniques, a comprehensive and accurate representation of both explicit indicators and implicit development states of enterprises is achieved. Relying on temporal mutation detection, multi-source data cross-verification, and adaptive weight suppression mechanisms, artificially manipulated abnormal data is effectively identified and filtered. Simultaneously, through dual-indicator distribution shift detection, continuous learning, and a multi-model hot-switching architecture, it adapts hierarchically to scenarios of gradual environmental change and structural mutation. Combined with digital twin simulation to complete multi-condition pre-training, the realism, stability, and overall generalization ability of the model's representation of enterprise states are significantly improved.

[0008] This invention establishes a closed-loop system encompassing latent state probability inference, uncertainty quantification, and policy effect feedback correction. It accurately quantifies inference credibility and iteratively optimizes model parameters based on data from the implementation of the cultivation cycle, continuously reducing inference bias. By combining knowledge transfer and confidence constraints to refine state representation and standardize decision-making logic, highly robust global state features are ultimately input into a reinforcement learning module. This enables intelligent and precise deployment of tiered cultivation policies and scientific effect evaluation, effectively improving the intelligence level, execution efficiency, and actual cultivation results of enterprise tiered cultivation work. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the execution process of the method for precise delivery and effect evaluation of high-value enterprise gradient cultivation policies based on reinforcement learning provided in the embodiments of the present invention.

[0010] Figure 2 This is a schematic diagram of exemplary hardware and software components of the high-value enterprise gradient cultivation policy precision delivery and effect evaluation system based on reinforcement learning provided in this embodiment of the invention. Detailed Implementation

[0011] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a method for precise targeting and effectiveness evaluation of high-value enterprise gradient cultivation policies based on reinforcement learning, provided in one embodiment of the present invention. The following is a detailed description of this method for precise targeting and effectiveness evaluation of high-value enterprise gradient cultivation policies based on reinforcement learning.

[0012] Step S110: Classify and extract features from the enterprise's multi-source heterogeneous data. For structured time-series data, unstructured text data, and graph data, respectively, use corresponding feature learning algorithms to complete vectorization representation, and obtain unified low-dimensional basic state features through attention fusion. This embodiment addresses the issues of fragmented features, inconsistent dimensions, and poor fusion in multi-source heterogeneous data within the context of enterprise tiered development. It employs categorized data processing and refined feature extraction. For structured time-series data such as revenue, production capacity, R&D investment, and talent structure, a temporal convolutional network is used to extract local temporal features and cyclical evolution features. For unstructured textual data such as policy texts, enterprise application materials, and industry research reports, a pre-trained language model is used to complete semantic encoding and latent feature mining. For graph data such as industry chain connections and enterprise cooperation topologies, graph neural networks are used to mine node association features and topological structure features. After completing the independent vectorization representation of the three types of data, a multi-head attention mechanism is introduced to automatically allocate feature fusion weights from different data sources, adaptively strengthening high-value features and suppressing redundant noise features. Finally, these features are spliced ​​and fused to generate low-dimensional enterprise basic state features with unified dimensions and concise information.

[0013] Step S120: Construct a hierarchical composite state space, distinguish between continuously changing feature dimensions and discrete level dimensions, set state transition control logic, and adaptively adjust feature weights based on the enterprise's development level. This embodiment constructs a hierarchical composite state space adapted to the gradient growth pattern of enterprises. Based on the enterprise's development characteristics, it divides the space into continuously changing feature dimensions and discrete level dimensions, configuring differentiated state transition control logic. The continuously changing dimensions represent the enterprise's progressive growth characteristics such as operations, R&D, and talent, supporting smooth iterative updates. The discrete level dimensions correspond to the enterprise's gradient cultivation levels, triggering state transitions only when threshold conditions are met. Simultaneously, a hierarchical adaptive weight adjustment logic is introduced, dynamically adjusting the contribution weights of each feature dimension according to the enterprise's real-time cultivation gradient level, avoiding evaluation biases caused by fixed weights failing to adapt to different development stages. Through the combination of a hierarchical state structure and a dynamic weight mechanism, a refined and differentiated representation of enterprise growth states is achieved, balancing the gradual growth characteristics of enterprises with the abrupt changes in level transitions, improving the fit and accuracy of state expression.

[0014] Step S121: Decompose the overall state space of enterprise development into a continuous growth subspace and a discrete hierarchical subspace that are independent and interconnected and updated. Use real continuous vectors to model the continuous growth subspace to carry the progressive changes in enterprise operation, R&D technology, talent structure, production capacity, revenue and profit. This embodiment modularly decomposes the overall state space of enterprise development, constructing independent and interconnected continuously growing subspaces and discrete hierarchical subspaces. The continuously growing subspace uses high-dimensional real-number continuous vectors for modeling, specifically to represent the gradual changes in the enterprise's business development process. This includes continuous and smooth evolutionary indicators such as enterprise revenue, R&D technology iteration, talent structure optimization, production capacity expansion, and revenue and profit fluctuations. This subspace supports subtle iterations of feature values ​​with each data update, fully recording the enterprise's long-term growth accumulation and phased development changes, accurately depicting the enterprise's normalized operational growth patterns. Simultaneously, this subspace and the discrete hierarchical subspace are linked in real time.

[0015] Step S122: Solidify three types of gradient labels—innovative enterprises, specialized and innovative enterprises, and specialized and innovative small giant enterprises—within the discrete hierarchical subspace, and set a multi-dimensional hierarchical transition trigger threshold vector that includes R&D investment intensity, number of patents, revenue growth rate, industrial chain supporting capabilities, and talent scale, in combination with industry cultivation standards and historical sample data. This embodiment solidifies a gradient cultivation standard label system within a discrete hierarchical subspace, fixing three core gradient labels: innovative enterprises, specialized and innovative enterprises, and specialized and innovative "little giant" enterprises, thus constructing a standardized hierarchical classification benchmark. Based on national and local industry cultivation standards and massive amounts of historical enterprise cultivation sample data, a multi-dimensional hierarchical leap trigger threshold vector is constructed. The threshold dimensions specifically include five core dimensions: R&D investment intensity, number of authorized patents, annual revenue growth rate, supply chain matching capability, and core talent scale. The thresholds for each dimension are set differently based on the benchmark levels of enterprises in different industries and at different development stages, avoiding judgment bias caused by uniform thresholds. This threshold vector serves as the core judgment basis for enterprise hierarchical leaps and is solidified throughout the discrete hierarchical subspace.

[0016] Step S123: Configure the hierarchical state gating unit to read the feature values ​​of the continuously growing subspace in real time, continuously compare the current feature vector with the hierarchical transition trigger threshold vector, and when multiple features continuously meet the threshold conditions and reach the preset stable period, trigger the gating unit to complete the discrete hierarchical label switching and hierarchical update, and embed the hierarchical offset feature vector in the global state vector to record the deviation data between the enterprise and the current hierarchical benchmark indicator and the adjacent hierarchical indicators; This embodiment configures a hierarchical state gating unit to achieve intelligent updates of enterprise gradient levels. The gating unit reads various feature values ​​from the continuously growing subspace in real time and continuously compares the real-time feature vector with the preset level transition trigger threshold vector dimension by dimension. The system abandons the coarse judgment logic of transitioning as soon as a single cycle is met, and sets a preset stable cycle constraint. Only when multiple core features continuously and stably meet the corresponding level threshold conditions and there are no short-term abnormal fluctuations, the gating unit is triggered to complete the switching of discrete level labels and the level state update. After the level update is completed, the level offset feature vector is embedded in the global state vector at the same time to accurately record the deviation of the enterprise's current indicators from the benchmark indicators of this level and the threshold indicators of adjacent levels, quantifying the enterprise's level adaptability and transition potential, and realizing a refined and differentiated representation of the level state.

[0017] Step S124: Construct a hierarchical feature weight mapping relationship, dynamically allocate feature weights according to the gradient level of the enterprise, increase the feature weights corresponding to revenue growth rate, capacity expansion, R&D investment growth rate and talent expansion speed for innovative enterprises, and increase the feature weights corresponding to technology patent barriers, irreplaceability of industrial chain, technology scarcity, R&D quality and operation stability for specialized and innovative enterprises and specialized and innovative small giant enterprises. This embodiment constructs a dynamic mapping relationship between enterprise tier levels and feature weights, adaptively allocating the weights of each feature dimension based on the enterprise's current tier level. For entities in the innovative enterprise cultivation stage, the system adaptively increases the weights of scale-growth characteristics such as revenue growth rate, capacity expansion rate, R&D investment growth rate, and talent team expansion speed, focusing on the enterprise's rapid expansion and initial growth capabilities. For specialized and innovative enterprises and specialized and innovative "little giant" enterprises, the system dynamically decreases the weights of scale expansion characteristics while simultaneously increasing the weights of high-quality development characteristics such as the height of technological patent barriers, the irreplaceability of the industrial chain, the scarcity of core technologies, the quality of R&D output, and long-term operational stability, aligning with the cultivation and evaluation guidelines for enterprises at different tiers, and achieving precise, scenario-based, and hierarchical adaptation of feature weights.

[0018] Step S125: Synchronize the discrete hierarchical labels of the enterprise in real time and update the weight coefficients of each feature dimension of the continuous growth subspace. Perform normalization processing on the continuous growth subspace features, discrete hierarchical label features, and hierarchical offset features respectively. Concatenate the three types of features to obtain a composite state vector and complete the construction of the hierarchical composite state space.

[0019] This embodiment completes the final construction and feature fusion of a hierarchical composite state space, synchronously updating the state parameters of the enterprise's discrete hierarchical labels in real time, and iteratively updating the weight coefficients of each feature dimension of the continuous growth subspace based on the latest hierarchical results. Global normalization is performed on the optimized continuous growth subspace features, discrete hierarchical label features, and hierarchical offset features to eliminate differences in the dimensions and numerical deviations of features across different dimensions, ensuring that all features are within a unified numerical range. The three types of normalized feature vectors are concatenated dimensionally to generate a composite state vector that integrates the enterprise's progressive growth state, gradient hierarchical state, and hierarchical deviation state, completing the closed-loop construction of the hierarchical composite state space and achieving a multi-dimensional, multi-level, and high-precision global state representation of the enterprise.

[0020] Step S130: Decompose the internal dynamic factors and external dynamic factors of the system and integrate them into the state expression, monitor the data distribution shift in real time, and identify and suppress the interference of human abnormal data on feature expression; This embodiment constructs a dynamic disturbance adaptive perception and noise suppression module to achieve dynamic environmental adaptation and abnormal feature purification based on a composite state space. By decoupling and decomposing internal and external disturbance factors, the internal evolutionary fluctuations of the enterprise and external industrial policy disturbances are integrated as independent dynamic features into the basic state expression of the enterprise, improving the dynamic representation capability of the state space. Simultaneously, an online distribution offset detection mechanism is built to monitor the drift of feature data distribution in real time, distinguishing between gradual environmental changes and structural abrupt changes to complete adaptive adaptation. A supporting branch for identifying and suppressing enterprise strategic data spoofing is constructed to accurately identify abnormal features caused by artificial data manipulation. An adaptive weight decay mechanism filters out artificial noise interference, preserving the true operational fluctuation characteristics of the enterprise, ultimately improving the robustness and authenticity of the enterprise's global state expression.

[0021] Step S131: Perform internal and external binary decoupling classification on the disturbance factors of the enterprise gradient cultivation system, construct the corresponding dynamic factor variable pool, model the enterprise's endogenous evolution disturbance and the macroeconomic policy and industrial environment exogenous disturbance separately, and integrate them into the enterprise's basic state vector as independent feature dimensions. This embodiment decouples various disturbance factors in the enterprise's tiered development system into a binary system, constructing a dynamic factor variable pool with internal and external dimensions. Internal dynamic factors focus on the enterprise's endogenous evolutionary behavior, encompassing endogenous variables such as quarterly operational fluctuations, technological iteration progress, R&D structure adjustments, talent mobility changes, policy and qualification application changes, and phased operational strategy adjustments, depicting the dynamic changes in the enterprise's autonomous growth. External dynamic factors focus on disturbances in the system's external environment, including environmental variables such as fluctuations in fiscal support budgets, adjustments in local industrial guidance, iterations of regional competitor support policies, macroeconomic cycle fluctuations, supply and demand fluctuations in upstream and downstream supply chains, changes in industry access rules, and changes in regional industrial resource supply. These two types of dynamic factors are embedded as independent feature dimensions into the enterprise's basic state vector, achieving a structured and explicit representation of dynamic disturbance information.

[0022] Step S132: Construct an online distribution shift detection mechanism that integrates two indicators. Using the initial training distribution as a benchmark, compare the differences in online feature distributions in real time and quantify the degree of drift. Through graded thresholds, achieve graded identification and adaptation triggering of gradual environmental changes and structural abrupt changes. This embodiment constructs an online distribution shift detection system that integrates the maximum mean difference and the marginal distribution shift rate as dual indicators. Using the enterprise state feature distribution during the initial model training phase as a benchmark, the benchmark mean vector and feature covariance matrix are fixed. During the online inference phase, real-time enterprise state feature data is collected in batches, and the maximum mean difference shift index and the marginal distribution shift rate between the real-time data and the benchmark distribution are calculated respectively, thus doubly quantifying the degree of distribution drift in the state space. The system presets two levels of shift thresholds: mild and severe. When a mild, slow shift is detected, it marks a gradual change in the environment and triggers an incremental model adaptation strategy. When a severe structural distribution change is detected, it triggers a global adaptation warning and a model branch switching mechanism, achieving hierarchical identification, hierarchical response, and adaptive adaptation to dynamic environmental changes.

[0023] Step S133: Build a branch for identifying enterprise strategic data spoofing, and combine time-series mutation detection with cross-verification of multi-source objective data to identify abnormal indicator characteristics of enterprises that are artificially modified or deliberately inflated; This embodiment establishes an independent branch for identifying deceptive corporate strategic indicators, specifically targeting the deliberate manipulation and inflation of indicators by enterprises to obtain policy support. This addresses the issue of distorted data reporting leading to biased state representation. Based on the temporal variation patterns of core performance indicators for enterprise development and objective regulatory data, a two-layer identification architecture is constructed: temporal anomaly detection and multi-source cross-verification. First, suspected manipulated data is screened through anomalies in temporal features. Then, accurate identification is achieved through comparison with authoritative objective data, distinguishing between normal operational fluctuations and deliberate manipulation. Strategic noise data is precisely eliminated, ensuring the authenticity and reliability of the enterprise's state characteristic input.

[0024] Step S1331: Select the revenue, R&D investment, number of patents, talent scale, and R&D equipment investment assessment indicators corresponding to the enterprise's tiered development. Conduct stationarity modeling on the historical time series of each indicator. Capture the fluctuation amplitude of the indicators periodically through the calculation method of first-order time series difference and second-order curvature change. Fit the normal growth range and normal fluctuation threshold range corresponding to each indicator. Identify single-cycle jumps, cliff-like drops, and step-like mutation samples without continuous trend support. Mark abnormal indicator segments that deviate from the long-term growth pattern of the enterprise as suspected strategic modification data. Preliminarily distinguish between normal operating fluctuations and abnormal fluctuations of the enterprise. This embodiment selects high-frequency modified performance indicators such as revenue, R&D investment, number of patents, talent pool size, and R&D equipment investment to conduct time-series stationarity modeling and abnormal mutation identification. The continuous time-series sequences of each indicator are decomposed and analyzed. The periodic fluctuation amplitude of the indicators is calculated using first-order time-series differencing, and the abrupt changes in the growth trend of the indicators are captured using second-order curvature changes. The normal growth range and normal fluctuation threshold range of each indicator are fitted to obtain accurate results. Abnormal samples such as sudden jumps, precipitous drops, and step-like anomalies without continuous trend support in a single period are accurately identified. Indicator segments that deviate from the long-term growth patterns of the enterprise are marked as suspected strategically manipulated data, thus initially completing the screening of abnormal data and distinguishing between natural operational fluctuations and deliberate data manipulation from the perspective of time-series patterns.

[0025] Step S1332: Construct a multi-source objective data cross-verification link, establish a cross-source indicator verification mapping relationship between enterprise self-reported data and industrial chain knowledge graph data, tax invoicing data, social security participation data, patent publicity data, enterprise bidding and production capacity operation data, and for suspected abnormal indicators obtained from the time series initial screening, retrieve the corresponding multiple objective authoritative data sources for true value comparison, and conduct cross-source cross-verification of enterprise reported R&D investment, number of reported talents, number of patent applications, and revenue growth data respectively; This embodiment establishes a multi-source objective data cross-verification link, completely overcoming the limitations of data submitted by a single enterprise, and establishing a cross-source verification mapping system based on multi-dimensional authoritative data sources. It integrates objective and authoritative data sources such as industry chain knowledge graphs, tax invoicing ledgers, social security participation data, data published by the State Patent Office, enterprise bidding records, and production capacity monitoring data to construct a one-to-one correspondence between submitted indicators and objective data. For suspected abnormal indicators obtained from the initial time-series screening, it automatically retrieves objective data from the same source for true value comparison, achieving cross-source cross-verification of core indicators such as R&D investment, talent quantity, patent applications, and revenue growth, thus verifying the authenticity of the submitted indicators through objective data.

[0026] Step S1333: Based on the cross-source comparison results, complete the abnormal data judgment. The indicator changes that have significant deviations, rely solely on data reported by the enterprise and lack objective data to support them are judged as strategically disguised data. The indicator changes that have controllable fluctuation ranges and can be effectively supported by multi-source objective data are judged as the enterprise's real operational fluctuation data. This embodiment uses cross-verification results from multi-source data to accurately identify and distinguish between genuine and fake data, forming a standardized judgment logic. For indicator changes with significant abnormal fluctuations over time, substantial increases in enterprise-reported data, but lacking supporting objective data such as tax, social security, patent, or supply chain orders, and exhibiting significant deviations from normal industry development patterns, these are judged as strategically manipulated data deliberately inflated or artificially embellished by the enterprise. For indicator changes with controllable fluctuations, continuous and stable time-series evolution, and effective corroboration from multi-source objective data, these are judged as genuine business growth and normal development fluctuations. This two-layer verification mechanism achieves precise separation between abnormal data and genuine business data, effectively filtering data manipulation interference in policy application scenarios.

[0027] Step S1334: Construct an anomaly feature localization and source tracing and confidence scoring mechanism to accurately identify the indicator dimensions, modification time nodes and modification magnitudes corresponding to strategic modifications. Combine the degree of temporal mutation, the magnitude of deviation of multi-source data, and the multiple-dimensional factors of anomaly duration to comprehensively calculate and generate an indicator anomaly modification confidence score.

[0028] This embodiment constructs an anomaly feature localization and source tracing mechanism with confidence scoring to achieve quantitative evaluation and precise source tracing of strategic data manipulation. It accurately identifies the specific indicator dimension where artificial manipulation occurred, the time point of the manipulation, and the magnitude of the manipulation. Simultaneously, it extracts three core factors: the severity of temporal mutations, the magnitude of multi-source data deviation, and the duration of the anomaly. A weighted fusion algorithm is used to comprehensively calculate the confidence score of the anomaly manipulation. A higher confidence score indicates a stronger credibility of the artificial manipulation and a higher degree of data distortion. This quantitative scoring result directly serves as the basis for adjusting the subsequent adaptive feature decay weights, achieving quantitative suppression of anomalous noise.

[0029] Step S134: Establish an adaptive feature decay suppression mechanism, dynamically adjust the weight of abnormal features according to the degree of abnormal deviation, retain the normal operating fluctuation characteristics of the enterprise, and complete the adaptive filtering and feature correction of strategic noise; This embodiment establishes an adaptive dynamic feature decay weight mechanism to intelligently filter and correct feature noise generated by strategic data masquerading. Based on the anomaly modification confidence score and the degree of data deviation, the system adaptively assigns corresponding decay suppression coefficients to each anomalous feature dimension. The higher the anomaly deviation and modification confidence, the stronger the corresponding feature weight suppression, significantly weakening the interference of distorted data on state representation. Simultaneously, it completely preserves the original weights of normal fluctuations generated by the natural evolution of the enterprise's operations, without interfering with the feature expression of the enterprise's true growth data. During each round of global state vector update, it automatically completes the reconstruction of anomalous feature weights and global normalization correction, achieving adaptive filtering of strategic noise and precise feature correction.

[0030] Step S135: Construct a multi-factor linkage state update mechanism, integrate dynamic factor fluctuations, distribution drift quantification results, and anomaly correction information into the global state expression, and combine static and dynamic features to generate a highly robust enterprise state representation.

[0031] This embodiment constructs a multi-factor linked global state update mechanism to achieve deep integration of multi-dimensional information and the generation of highly robust state representations. The system synchronously embeds three types of dynamic information—internal and external dynamic factor fluctuation values, feature distribution shift quantification index, and abnormal feature suppression and correction results—into the enterprise's global state space, updating the dynamic perturbation components of the state vector in real time. It integrates enterprise static growth characteristics, environmental dynamic change characteristics, data distribution drift information, and abnormal data correction information to achieve unified modeling and deep integration of multi-dimensional information. Through multi-factor linked updates, it completely avoids representation biases caused by data noise, environmental disturbances, and distribution shifts, ultimately generating a highly robust enterprise global state representation vector that combines stability, realism, and generalization.

[0032] Step S140: Adopt a continuous learning and multi-model switching strategy to adapt to dynamic changes in the environment, and at the same time build a digital twin simulation environment to complete model pre-training under multiple working conditions based on the simulation scenario; This embodiment constructs a three-pronged dynamic environment adaptation system encompassing slow-change adaptation, rapid-change switching, and simulation pre-training to address the insufficient generalization ability of models caused by dynamic changes in the industrial environment. For scenarios with gradual and slow changes in the macro environment, an incremental continuous learning strategy is adopted to achieve smooth iterative updates of model parameters, avoiding catastrophic forgetting. For scenarios with structural abrupt changes such as major policy changes and economic shocks, a multi-model branch hot-switching architecture is built to achieve rapid adaptation to operating conditions. Simultaneously, a digital twin simulation environment is built based on the enterprise's comprehensive business logic, generating simulation samples in batches across multiple operating conditions, disturbances, and policy scenarios. This completes pre-training of the model, compensating for the scarcity of extreme operating condition samples in real-world scenarios, and comprehensively improving the model's adaptability and generalization ability to dynamic industrial environments.

[0033] Step S141: Construct an incremental continuous learning adaptation mechanism for scenarios with slow and gradual changes in the macro environment, and use the elastic weight consolidation algorithm to solidify and protect the general enterprise cultivation features and basic representation parameters that have been trained in the basic model. This embodiment constructs an incremental continuous learning adaptation mechanism based on the elastic weight consolidation algorithm, enabling the model to smoothly adapt to gradual environmental changes. Weights are solidified and access protected for the core network parameters corresponding to the general enterprise cultivation features and basic representation capabilities already trained in the basic model. Parameter update constraints are set to prevent new data from overwriting existing mature parameters during iterative training, completely resolving the catastrophic forgetting problem in model iteration. Only the branch network parameters specifically responsible for dynamic environmental adaptation are allowed to participate in incremental updates. While retaining the model's original general feature extraction capabilities, this achieves smooth iterative adaptation to scenarios such as fine-tuning of industrial policies, slow industry changes, and gradual changes in the market environment.

[0034] Step S142: After the end of each policy cultivation cycle, collect the latest enterprise operation data, industry environment data and policy implementation data for the current period. Only perform small-batch incremental iterative updates on the branch network parameters that dynamically adapt to the corresponding environment of the model. While retaining the original general feature extraction capability of the model, it adaptively adapts to the gradual changes in data distribution brought about by industrial policy iteration, industry changes and market fine-tuning. This embodiment uses the policy cultivation cycle as an iterative node to conduct incremental model parameter updates and optimizations. After the end of each quarterly and annual policy cultivation cycle, new enterprise operating data, industrial environment monitoring data, and policy implementation effectiveness data are collected in batches to construct a new time-series training sample set. A mini-batch gradient descent algorithm is used to incrementally update only the parameters of the environment adaptation branch network, making minor adjustments to the model's dynamic feature mapping rules without requiring global model retraining. Through periodic incremental updates, the model gradually adapts to the progressive changes in data distribution brought about by industrial policy iterations, industry technology changes, and market supply and demand fine-tuning, ensuring a smooth, accurate, and forgetful model adaptation process.

[0035] Step S143: For environmental change scenarios such as economic shocks, major policy revisions, and structural changes in industries, build a hot-switching adaptation architecture for model branches. Based on multiple types of historical macroeconomic data sets, pre-train independent sub-model branches that adapt to different scenarios such as normal development, policy easing, policy tightening, economic downturn, and industrial restructuring. Each sub-model branch independently stores the network parameters and feature mapping rules corresponding to the scenario. This embodiment establishes a hot-swap model branch adaptation architecture for scenarios with structurally abrupt environmental changes, enabling rapid adaptation to extreme operating conditions. Based on a massive historical macroeconomic operating condition dataset, five typical macroeconomic scenarios—normal development, policy easing, policy tightening, economic downturn, and industrial restructuring—are identified. Dedicated sub-model branches are trained independently for each scenario, learning the feature mapping patterns of their respective scenarios and independently storing their network parameters and weight configurations. These sub-model branches are deployed in parallel in the system backend, operating independently without interference and capable of responding to operating condition switching needs at any time.

[0036] Step S144: Real-time collection of dynamic environmental factor data and input into the scene matching discriminator. Through environmental factor feature similarity calculation, the optimal sub-model branch corresponding to the current real macroscopic working condition is matched to achieve rapid hot switching of model branches without running interruption or retraining, adapting to the structural distribution drift of the environment. This embodiment constructs a real-time operating condition matching and model hot-switching mechanism to achieve millisecond-level adaptation to structural environmental drift. The system collects dynamic factor data of various macro-environmental factors in real time, inputs them into a preset scene matching discriminator, and calculates the matching degree between the current environmental features and the corresponding scene features of each sub-model using a cosine similarity algorithm. This accurately selects the optimal sub-model branch that best fits the current real macro-environmental operating conditions. Under the premise of uninterrupted system operation and uninterrupted business, seamless hot-switching of model branches is completed without retraining or parameter fine-tuning. This rapidly adapts to structural data distribution drift caused by economic shocks, policy changes, and industrial restructuring, significantly shortening the model adaptation iteration cycle.

[0037] Step S145: Based on the enterprise's full-cycle historical cultivation data, industry operation rules, policy implementation logic and supply chain linkage mechanism, construct a digital twin simulation environment for enterprise gradient cultivation, digitally replicate the endogenous evolutionary behavior of enterprise operation and growth, technology iteration, talent changes and production capacity iteration, and simulate and restore the exogenous environmental changes scenarios such as fiscal adjustments, macro fluctuations, changes in industry competition, supply chain shocks and regional support policy iterations; This embodiment establishes a dedicated digital twin simulation environment for enterprise tiered development, achieving a full-dimensional digital replication of the enterprise's internal behaviors and external environment. Based on the enterprise's full-cycle historical development data, underlying industry operation rules, policy implementation logic, and supply chain linkage mechanisms, a simulation framework is constructed to accurately replicate the enterprise's internal evolutionary behaviors, including business growth, technological iteration, talent mobility, and production capacity iteration. Simultaneously, it digitally recreates external environmental changes such as adjustments to fiscal support, macroeconomic fluctuations, changes in industry competitive landscape, supply chain supply and demand shocks, and updates to regional support policies, achieving a one-to-one digital replication of real-world development business scenarios.

[0038] Step S146: By configuring simulation parameters and disturbance thresholds, it supports simulation of single-variable disturbances and multi-variable combined disturbances, covering endogenous conditions such as normal enterprise growth, phased rapid development, strategic indicator modification, and short-term operational fluctuations, as well as exogenous conditions such as stable development, policy changes, economic shocks, and industry reshuffling. This embodiment configures parameters and expands operating conditions for the digital twin simulation environment, building a comprehensive simulation disturbance capability. Through fine-tuning of various simulation parameters and disturbance thresholds, it supports simulation experiment modes for both single-variable independent disturbances and multi-variable combined collaborative disturbances, flexibly simulating various complex business scenarios. The simulation operating conditions comprehensively cover endogenous enterprise behaviors such as normal and stable growth, rapid phased transitions, strategic indicator adjustments, and short-term operational fluctuations, while also covering all dimensions of exogenous environmental conditions such as stable industry development, major policy changes, macroeconomic shocks, and industry reshuffling, filling the gaps in real-world extreme and rare operating condition samples.

[0039] Step S147: Generate a large number of simulation samples under multiple enterprise development statuses, multiple environmental disturbances, and multiple policy conditions based on the digital twin simulation environment. Use the multi-condition simulation samples to conduct pre-training of the model and learn the feature mapping rules under various extreme scenarios and rare conditions. This embodiment utilizes a digital twin simulation environment to conduct pre-training of a multi-condition model, enhancing its generalization ability. In the simulation environment, the enterprise's development status, environmental parameters, and policy variables are randomly perturbed, generating a massive number of differentiated simulation samples covering various operating conditions, including normal scenarios, extreme scenarios, rare perturbation scenarios, and policy change scenarios. These massive multi-condition simulation samples are input into the model for pre-training, allowing the model to learn the feature mapping patterns under different environmental perturbations, policy conditions, and enterprise states. This compensates for the insufficient model fitting caused by the limited number of extreme samples in real-world scenarios, strengthening the model's adaptability to complex scenarios.

[0040] Step S148: By aligning the rules and calibrating the parameters between the simulation environment and the real business scenario, the Sim2Real distribution gap between the simulation and the real scenario is reduced. Combined with incremental continuous learning and model branch hot switching mechanism, a comprehensive dynamic environment adaptation system is formed, which includes slow incremental adaptation, fast branch switching, and full-condition simulation pre-training.

[0041] This embodiment performs alignment calibration between simulation and real-world scenarios, constructing a comprehensive dynamic environment adaptation system. It continuously iterates and calibrates the rule parameters, perturbation thresholds, and variable distributions of the digital twin simulation environment, narrowing the distribution differences between simulation samples and real business data, reducing the Sim2Real adaptation gap, and ensuring that the simulation pre-training results can be effectively transferred to real-world business scenarios. Combining the slow-change adaptation capability of incremental continuous learning, the fast-change adaptation capability of hot-switching model branches, and the generalization capability of multi-condition simulation pre-training, a three-in-one comprehensive dynamic environment adaptation system is formed, enabling the model to operate stably and accurately in various dynamic, complex, and extreme scenarios.

[0042] Step S150: Establish a probabilistic inference model for the unobservable latent variables of the system, solve the latent state distribution by combining time series observation data, and quantify the uncertainty of the latent state inference; This embodiment addresses the latent states that cannot be directly observed in a company's tiered development system by constructing a probabilistic inference and uncertainty quantification system, overcoming the limitations of traditional explicit indicator modeling. Based on observable time-series operational data, a time-series probabilistic latent state inference model is built. Through the linkage of observation equations and state transition equations, the latent development state of the company is inferred, outputting a continuous probability distribution of latent states rather than a single fixed value. Simultaneously, inference errors are quantified from both cognitive and stochastic uncertainties, accurately characterizing the credibility and fluctuation range of latent state inference. This enables a probabilistic and refined representation of the company's latent technological potential, growth momentum, and potential risks, solving the problems of one-sided state representation and uncontrollable credibility in traditional models.

[0043] Step S151: Construct a latent variable system for enterprise gradient cultivation scenarios, define latent factors that cannot be directly observed and collected, such as the enterprise's real technological potential, long-term growth potential, industrial resource adaptability, policy absorption and transformation capabilities, latent operational risks, and future leap potential, and build a latent variable probability inference framework adapted to enterprise cultivation scenarios. This embodiment constructs a comprehensive system of latent variables for enterprise tiered development and establishes a dedicated framework for probabilistic inference of latent states. Combining the characteristics of enterprise development business, it defines multiple categories of latent core factors that cannot be directly observed or collected, specifically including six latent state variables: the enterprise's true technological potential, long-term growth momentum, industry resource adaptability, policy absorption and transformation capability, latent operational risks, and future potential for hierarchical advancement. It abandons the traditional rigid model that relies solely on explicit indicators for modeling, and constructs a probabilistic inference framework for latent variables adapted to the enterprise tiered development scenario. This enables in-depth mining from explicit observation data to latent development states, comprehensively depicting the deep-seated development characteristics of enterprises.

[0044] Step S152: Based on the observable time-series indicators of enterprise operation, R&D, talent, production capacity, and industrial chain, build a time-series probabilistic hidden state inference model, construct observation equations and state transition equations, establish the mapping relationship between explicit observation indicators and the hidden state inside the enterprise through the observation equations, and characterize the time-series evolution law of the hidden state with the changes of policy cycle, business cycle, and industrial environment through the state transition equations. This embodiment constructs a time-series probabilistic latent state inference model and establishes a mechanism for inferring the time-series evolution of latent states. Using a series of observable time-series indicators across multiple dimensions—including enterprise operations, R&D, talent, production capacity, and supply chain connections—as input, it constructs bidirectional, interconnected observation equations and state transition equations. The observation equations establish a precise mapping relationship between multidimensional explicit observation indicators and the enterprise's internal latent states, enabling the derivation of latent states from explicit data. The state transition equations characterize the dynamic evolution of latent states with policy cycles, enterprise operating cycles, and changes in the industrial environment, simulating the continuous iteration and dynamic changes of the enterprise's latent capabilities, thus forming a complete time-series latent state inference logic.

[0045] Step S1521: Standardize and preprocess the observable indicators of enterprise operation, R&D, talent, production capacity and industrial chain linkage dimensions, unify the dimensions of each indicator and the data sampling period, and organize them into a continuous multi-dimensional time series observation sequence according to the policy statistical cycle. Remove missing values ​​and outliers from the sequence to form a well-organized and time-aligned set of observation variables, which serves as the data source for model input. This embodiment performs standardized preprocessing on the multidimensional time-series data input to the model to ensure the regularity and temporal alignment of the input data. It unifies the dimensional system and data sampling period for various indicators related to operations, R&D, talent, production capacity, and the industrial chain, and reconstructs scattered data according to policy statistical cycles to form a continuous, equally spaced multidimensional time-series observation sequence. Through preprocessing operations such as missing value interpolation and imputation, outlier removal, and temporal alignment correction, it addresses issues such as scattered, missing, abnormal, and misaligned original data, generating a set of observation variables with high completeness, strong temporal consistency, and good standardization.

[0046] Step S1522: Complete the system state hierarchy division and variable definition, define the enterprise's technological potential, growth potential, resource adaptability, policy transformation capability, and implicit operational risks that cannot be directly collected and observed as implicit state variables, determine the dimension and value space of the implicit state vector, and achieve accurate division between observable variables and implicit state variables. This embodiment completes the hierarchical division and precise definition of model variables, constructing a clear state modeling system. It clearly distinguishes between observable and latent state variables, using pre-processed enterprise operation, R&D, talent, production capacity, and supply chain indicators as the set of observable variables; and defining enterprise technological potential, growth momentum, resource adaptability, policy transformation capability, and latent operational risks as core latent state variables. It precisely defines the dimensional range and value space of the latent state vector, clarifying the business meaning and representation range of each latent variable, achieving complete decoupling and precise division between observed and latent variables.

[0047] Step S1523: Construct the model observation equation, establish the correlation between the multidimensional observation indicators at each time point and the hidden state vector at the same time through linear or nonlinear mapping functions, introduce the observation noise term that conforms to the preset probability distribution, simulate the random error in the data collection, filling and statistical process, and establish the positive mapping relationship between the explicit observation data and the hidden state inside the enterprise. This embodiment constructs a model observation equation to achieve a positive mapping from explicit data to implicit states. A linear and nonlinear fusion mapping function adapted to enterprise cultivation scenarios is designed to establish the corresponding relationship between multi-dimensional observation indicators and the corresponding implicit state vector at each time step. An observation noise term following a Gaussian distribution is introduced to accurately simulate random errors and human noise generated during data collection, enterprise reporting, and platform statistics, closely reflecting the error characteristics of real business data. Through the observation equation, the implicit state can be accurately derived from the observation data at any time step, constructing a stable and reliable explicit-implicit state mapping link.

[0048] Step S1524: Construct the model state transition equation, combine the business operation rules, policy iteration rhythm and industrial environment evolution characteristics, build the hidden state time series evolution function, take the hidden state vector of the previous moment, the current policy environment factor and industry prosperity factor as input, output the hidden state vector of the current moment, and add state transition noise to represent the external environment disturbance and the uncertainty of enterprise development, and characterize the time series dynamic evolution logic of the hidden state. This embodiment constructs a hidden state transition equation to characterize the temporal evolution of a company's hidden state. Combining the company's normal operational growth patterns, the pace of regional policy iterations, and the characteristics of industrial environment evolution, a dynamic temporal evolution function is designed. Using the hidden state vector of the previous moment, current policy environment factors, and industry prosperity factors as input variables, the hidden state vector of the current moment is accurately deduced. Simultaneously, a state transition noise term is added to quantitatively represent the random disturbances in the external environment and the uncertainty of the company's own development, fully reproducing the dynamic process of the company's hidden capabilities iterating over time, ensuring that the evolutionary pattern closely matches real-world industry development scenarios.

[0049] Step S1525: Based on historical industry samples and benchmark enterprise data, initialize the weight coefficients, bias terms, and noise distribution parameters of the observation equation and state transition equation. Use the maximum likelihood estimation method to substitute the segmented time series samples for iterative training and optimization, and correct the model mapping relationship and state evolution rules. This embodiment completes model parameter initialization and iterative optimization to improve model fitting accuracy. Based on massive historical samples from the industry and benchmark enterprise cultivation data, it initializes the weight coefficients, bias terms, and noise distribution parameters of the observation equation and state transition equation, ensuring that the initial parameters conform to the general development patterns of the industry. Segmented time-series samples are batch-substituted into the model, and the maximum likelihood estimation algorithm is used to perform multiple rounds of iterative training and backpropagation on all model parameters. This continuously corrects the feature mapping relationship and hidden state evolution rules, constantly reducing the deviation between the model's inferred values ​​and the actual enterprise development state, thereby improving the overall model fitting accuracy.

[0050] Step S1526: After training, fix the model structure and all parameters, access the enterprise's real-time time series observation data, and at each time step, associate the current observation value with the hidden state through the observation equation. At the same time, combine the historical hidden state and environmental variables through the state transition equation to deduce the current hidden state, forming a closed-loop time series probabilistic hidden state deduction link, dynamically depicting the evolution and changes of the enterprise's hidden state with policy cycles, business cycles, and industrial environment.

[0051] This embodiment completes model solidification and real-time time-series extrapolation, achieving dynamic perception of latent states. After model training converges, the overall network structure and all parameters are fixed, and real-time time-series observation data from the enterprise is integrated to initiate routine time-series extrapolation. In each time step, the observation equation links the current observation data with the latent state, while the state transition equation extrapolates the current state based on historical latent states and environmental variables. These two equations work together to form a closed-loop time-series probabilistic latent state extrapolation link. This continuously and dynamically characterizes the evolution of various latent capabilities and potential risks of the enterprise with policy cycles, business cycles, and the industrial environment, achieving real-time and accurate perception of the enterprise's latent state.

[0052] Step S153: Use the temporal variational inference algorithm to process non-stationary, weakly periodic, and noisy enterprise cultivation time series data. The encoder maps the time series observation data to the latent space, outputs the mean vector and variance vector corresponding to the latent state, solves the Gaussian posterior distribution of the latent state, and obtains the probability distribution interval of the enterprise latent state at each time. This embodiment employs a temporal variational inference algorithm to solve for the posterior distribution of the latent state, adapting to the characteristics of enterprise development time-series data. Addressing the non-stationarity, weak periodicity, noise contamination, and weak regularity of enterprise time-series data, a deep encoder maps high-dimensional time-series observation data to a low-dimensional latent space, outputting the mean and variance vectors corresponding to the latent states. Based on the mean and variance parameters, a Gaussian posterior distribution of the latent state is constructed, yielding the complete probability distribution interval of the enterprise's latent state at each time step. This overcomes the shortcomings of traditional models with fixed single-point outputs, comprehensively characterizing the fluctuation range and distribution features of the enterprise's latent state, and accurately reflecting the uncertainty characteristics of latent state inference.

[0053] Step S154: Construct a two-dimensional uncertainty quantification branch to calculate the cognitive uncertainty caused by insufficient model parameter fitting and lack of extreme working condition samples, as well as the random uncertainty caused by random disturbances in the industrial environment and short-term operational fluctuations of enterprises. The two types of uncertainty are then fused to obtain the overall uncertainty score for the hidden state inference. This embodiment constructs a two-dimensional parallel uncertainty quantification branch to achieve refined quantification of hidden state inference errors. Uncertainty sources are separated into two dimensions: model inherent defects and external environmental disturbances, with independent computational chains for cognitive uncertainty and stochastic uncertainty constructed separately. Cognitive uncertainty focuses on systematic biases caused by inadequate model parameter fitting and missing extreme condition samples; stochastic uncertainty focuses on random biases caused by random oscillations in the industrial environment and short-term operational fluctuations in enterprises. Through independent calculation and weighted fusion of these two types of uncertainty, a unified overall uncertainty score is generated, comprehensively quantifying the credibility and error level of each round of hidden state inference.

[0054] Step S1541: Dimensionally decompose the sources of uncertainty in the hidden state inference process, define the calculation boundaries, influencing factors and data sources of cognitive uncertainty and random uncertainty, build a two-dimensional parallel uncertainty quantification calculation branch, and connect to model parameters, simulation samples, industrial environment time series data and enterprise operation data respectively; This embodiment completes the decomposition of uncertainty dimensions and the construction of the computational architecture, clearly defining the quantification standards. It precisely defines the computational boundaries, influencing factors, and corresponding data sources for cognitive uncertainty and stochastic uncertainty, establishing a dual-dimensional parallel uncertainty quantification computation branch. The cognitive uncertainty computation branch connects to the model network parameters and the simulation condition sample library to evaluate inference errors caused by insufficient model learning; the stochastic uncertainty computation branch connects to time-series data of the industrial environment and real-time data of enterprise operations to evaluate inference biases caused by external random disturbances. The two branches operate independently and do not interfere with each other.

[0055] Step S1542: Conduct cognitive uncertainty quantification calculation. In response to the inference error caused by model parameter fitting defects and missing extreme working condition samples, multiple rounds of random sampling of model network parameters are performed during the model inference stage. Multiple hidden state inferences are completed using different parameter subsets. The dispersion of multiple sets of hidden state inference results is statistically analyzed. The sample completeness correction coefficient is introduced in combination with the extreme working condition sample ratio in the historical sample library. The cognitive uncertainty value is obtained by weighting the parameter perturbation deviation and the sample completeness correction coefficient. This embodiment accurately quantifies cognitive uncertainty and assesses the inherent inference bias of the model. During the model inference phase, multiple rounds of random sampling of network parameters generate multiple sets of differentiated parameter subsets. Hidden state inference is performed independently using each set of parameters, resulting in multiple sets of hidden state distributions. The degree of discrete fluctuation of these multiple inference results is statistically analyzed, and the proportion of extreme conditions and niche scenarios in the historical sample library is combined to calculate a sample completeness correction coefficient. The parameter perturbation bias and the sample completeness correction coefficient are weighted and fused to obtain the final cognitive uncertainty value, quantifying the inference error caused by missing samples and insufficient parameter fitting.

[0056] Step S1543: Conduct quantitative calculation of stochastic uncertainty. For the inference error caused by random disturbances in the industrial environment and short-term operational fluctuations of enterprises, extract the industrial environment factors, supply chain data, and time-series indicators of enterprise operations in the same period, calculate the variance and oscillation amplitude of multi-period indicators, fit the interference intensity of external disturbances on the inference of the hidden state by combining the noise distribution parameters of the hidden state transition equation, and solve the inference bias corresponding to the random disturbance through probabilistic statistics to obtain the value of stochastic uncertainty. This embodiment quantifies random uncertainty and assesses the inference bias caused by external disturbances. It extracts real-time industry environmental factors, supply chain supply and demand data, and monthly operational time-series indicators of enterprises, calculating the variance and oscillation amplitude of multi-period indicators to quantify the intensity of random disturbances in the external environment and enterprise operations. Combining the pre-set noise distribution parameters of the hidden state transition equation, it fits the interference weights of various random disturbances on the hidden state inference, and uses probabilistic statistical algorithms to solve for the inference bias value corresponding to the random disturbance, accurately obtaining the numerical value of random uncertainty and characterizing the inference instability caused by environmental and operational fluctuations.

[0057] Step S1544: Based on the historical scenario verification results, calibrate the fusion weight coefficients of cognitive uncertainty and random uncertainty, perform weighted summation on the two types of uncertainty values, and normalize and map them to a unified numerical range to generate the overall uncertainty comprehensive score for hidden state inference. This embodiment achieves the fusion of two-dimensional uncertainties to generate a comprehensive uncertainty score. Based on numerous historical scenario verification experiments, the optimal fusion weight coefficients for cognitive uncertainty and random uncertainty are calibrated to accurately match the impact proportions of these two types of errors in enterprise development scenarios. The initial comprehensive uncertainty is obtained by multiplying the individual cognitive uncertainty and random uncertainty values ​​by their corresponding weights and then summing them. Global normalization is then applied to map the values ​​to a unified 0-1 range, generating a standardized overall uncertainty score for latent state inference, thus achieving unified quantification and horizontal comparison of uncertainty.

[0058] Step S1545: Preset the uncertainty classification confidence interval, match the corresponding high, medium and low confidence levels according to the overall uncertainty score, and output the cognitive uncertainty, random uncertainty sub-item values ​​and comprehensive uncertainty score simultaneously after each round of hidden state inference is completed; This embodiment constructs an uncertainty grading evaluation system to accurately determine the credibility of inferences. It presets three uncertainty confidence intervals—high, medium, and low—corresponding to different levels of credibility for hidden state inferences. After each round of hidden state inference, the system simultaneously outputs the numerical values ​​of cognitive uncertainty, random uncertainty, and the overall uncertainty score, automatically matching the corresponding confidence level. This grading mechanism intuitively reflects the accuracy and reliability of the hidden state inference in this round.

[0059] Step S1546: Continuously collect new working condition samples to supplement the sample library, dynamically update the sample completeness correction coefficient, iteratively weaken the model cognitive bias, and build a closed-loop linkage mechanism for uncertainty quantitative assessment and continuous model optimization.

[0060] This embodiment constructs a closed-loop linkage mechanism between uncertainty quantification and model optimization. The system continuously collects new real-world working condition samples and simulation extreme samples, dynamically supplementing the sample library, constantly enriching the scenario sample coverage, updating the sample completeness correction coefficient in real time, continuously weakening model cognitive bias, and gradually reducing cognitive uncertainty. Through routine sample updates, parameter iterations, and uncertainty retesting, a closed-loop optimization link of "sample supplementation—bias reduction—accuracy improvement" is formed, achieving deep linkage between uncertainty quantification assessment and continuous model iteration.

[0061] Step S155: Add a hidden state time series smoothing constraint mechanism. Based on the historical multi-period hidden state mean and evolution trend, regularize the current hidden state distribution to suppress the hidden state distribution deviation caused by single-period abnormal data and ensure the continuity of hidden state time series evolution. This embodiment adds a hidden state temporal smoothing constraint mechanism to ensure the continuity and stability of hidden state evolution. Based on the enterprise's historical multi-period hidden state averages and long-term evolution trends, temporal prior constraints are constructed to regularize the hidden state distribution obtained at the current moment. This effectively suppresses the hidden state distribution shift and abrupt fluctuations caused by single-period abnormal data and short-term random volatility, ensuring that the hidden state evolution trajectory conforms to the continuous growth pattern of the enterprise's gradient cultivation, avoiding state representation distortion caused by single-round abnormal samples, and improving the stability and rationality of the hidden state temporal evolution.

[0062] Step S156: Output the probability distribution of the enterprise's hidden state, the optimal hidden state estimate, and the quantitative index of inferred uncertainty on a cycle-by-cycle basis, and complete the probabilistic state characterization of the enterprise's hidden capabilities, potential risks, and growth potential.

[0063] This embodiment provides a comprehensive probabilistic representation of a company's latent state. At the end of each policy statistical cycle, the system automatically outputs the complete Gaussian probability distribution interval of the company's latent state, the optimal latent state estimate, the values ​​of individual uncertainties, and the quantitative score of the overall uncertainty metric. Through multi-dimensional output results, it comprehensively characterizes a company's latent technological capabilities, potential growth momentum, supply chain adaptability, policy transformation level, and latent operational risks, achieving a probabilistic, refined, and quantifiable representation of the company's deep-seated, unobservable characteristics.

[0064] Step S160: Combine information supplementation, confidence constraints, and knowledge transfer from similar samples to improve the expression of observation data and hidden states, and constrain subsequent decision-making behavior based on the inference confidence. This embodiment constructs a multi-dimensional state improvement and decision constraint mechanism to enhance the completeness of state representation and the rationality of decisions. It improves the representation of observed data and latent states through three methods: information supplementation, confidence constraints, and knowledge transfer from similar samples. For niche enterprise scenarios with missing data and sparse samples, it transfers mature feature mapping patterns from similar benchmark enterprises to fill the gaps in state representation. Simultaneously, it sets decision constraints based on the uncertainty score of latent state inference. High-confidence state results are allowed to be directly used for policy decisions and effect evaluations, while low-confidence results trigger a prudent decision-making mechanism. The decision input is optimized by combining manual review and data supplementation.

[0065] Step S170: Using the policy cultivation cycle as a node, the implicit state inference model is corrected in reverse using the implementation effect data to form a closed-loop iterative optimization mechanism; This embodiment constructs a closed-loop iterative optimization mechanism for the model based on the policy implementation effect, achieving long-term iterative improvement of model accuracy. Using the quarterly and annual policy implementation cycles for enterprise gradient cultivation as fixed iteration nodes, and relying on the real effect data after each round of policy implementation, the predictive accuracy of the latent state inference model is backtracked and verified. By comparing the deviation between the predicted latent state and the actual cultivation effect, model defects and fitting shortcomings are accurately located. Layered adjustments are made to the targeted correction of model parameters, supplementation of the sample library, and iteration of uncertain parameters, forming a fully automated closed-loop optimization chain of "prediction—implementation—feedback—correction," allowing the model to continuously adapt to dynamic industry and enterprise growth changes.

[0066] Step S171: Using the quarterly or annual policy implementation cycle of enterprise tiered cultivation as the iteration node, periodically backtrack and collect the actual implementation effect data of the current round of policy investment intensity, resource support amount, enterprise level leap results, revenue growth, patent increase, capacity improvement, and industrial chain support improvement. Construct a periodic backtracking sample set corresponding to policy input and enterprise response effect, and match and retain the hidden state inference results and uncertainty scores of the previous output of the current cycle to form a pair of training samples of hidden state prediction values ​​and actual cultivation effects. This embodiment conducts policy cycle data backtracking and paired sample construction to provide a data foundation for model iterative optimization. After each policy cultivation cycle ends, the system automatically backtracks and collects data on the actual implementation effects of this round of policies, such as the intensity of policy implementation, the amount of resource support, the results of enterprise level advancement, revenue growth, patent increase, capacity improvement, and improvements in industrial chain support, constructing a cycle-backtracking sample set of policy input and enterprise response effects. Simultaneously, it accurately retains the implicit state inference results and uncertainty scores from the early stages of this cycle.

[0067] Step S172: Construct a quantitative evaluation system for hidden state inference bias. Compare the hidden state parameters of enterprise technology potential, growth momentum, and policy absorption and transformation capabilities predicted at the beginning of the cycle with the actual leap performance, real growth increment, and policy implementation effect of enterprises at the end of the cycle. Calculate the single-cycle prediction bias, trend fitting error, and level matching error to quantify the degree of inference distortion of the model in this round and locate the enterprise type, industry conditions, and policy scenarios corresponding to high bias. This embodiment constructs a quantitative evaluation system for hidden state inference bias to accurately quantify model inference errors. It compares the hidden state parameters predicted by the model at the beginning of the cycle, such as enterprise technological potential, growth momentum, and policy absorption and transformation capabilities, with the actual hierarchical leaps, real growth increments, and policy implementation effectiveness at the end of the cycle. It calculates the absolute prediction bias for a single cycle, the time-series trend fitting error, and the hierarchical matching error, comprehensively quantifying the overall inference distortion of the model in this round. Simultaneously, it accurately categorizes the enterprise types, industry conditions, and policy scenarios corresponding to high-bias samples, precisely identifying model adaptation shortcomings and optimization directions.

[0068] Step S173: Establish a multi-dimensional hierarchical error back propagation correction mechanism, set differentiated model parameter update strategies for different error sources, supplement existing training samples with working conditions and simultaneously iterate the sample completeness coefficient and cognitive uncertainty parameter, fine-tune the evolution weight, environmental factor effect coefficient and noise distribution parameter of the state transition equation in reverse, and iterate and update the nonlinear mapping parameter of the observation equation at the same time. This embodiment establishes a multi-dimensional, hierarchical error backpropagation correction mechanism to achieve differentiated and precise model optimization. Dedicated correction strategies are set for different error sources. For cognitive biases caused by insufficient sample coverage or missing extreme conditions, new cultivation condition samples are added, and the sample completeness coefficient and cognitive uncertainty parameters are updated. For biases caused by environmental disturbances or inaccurate state evolution fitting, the evolution weights, environmental factor coefficients, and noise distribution parameters of the state transition equation are fine-tuned in reverse. For biases caused by distortion in the mapping between explicit and implicit states, the nonlinear mapping parameters of the observation equation are iteratively updated, achieving targeted and precise correction of multi-module parameters.

[0069] Step S174: Configure a multi-period iterative update constraint mechanism and set an error accumulation update threshold. Only when the same scenario generates inference deviation in the same direction for multiple consecutive periods and the accumulated error exceeds the preset threshold, the model parameters are formally updated. The historical effective fitting parameters are retained to avoid overfitting caused by random fluctuations in a single period and to ensure the stability and authenticity of model iterative optimization. This embodiment employs a multi-period iterative update constraint mechanism to ensure model iteration stability. A multi-period error accumulation update threshold is set, abandoning the coarse-grained approach of directly updating errors in a single period. Model parameter updates are only formally triggered when similar industry scenarios and enterprise types exhibit consecutive inference deviations in the same direction over multiple periods, and the accumulated error exceeds the preset threshold. During iteration, historically mature and effective fitting parameters are retained, with only the parameters of the modules corresponding to deviations being fine-tuned. This effectively avoids model overfitting caused by random fluctuations and accidental errors in a single period, ensuring the authenticity and stability of the model iteration optimization.

[0070] Step S175: Construct a fully automated closed-loop iterative chain for latent state prediction, policy implementation, effect feedback, error assessment, parameter correction, and uncertainty update. Continuously optimize the model by relying on the real effects of policy implementation to reduce the bias of latent state inference and enable the model to dynamically adapt to the industrial environment and enterprise growth patterns.

[0071] This embodiment constructs a fully automated closed-loop iterative chain to achieve long-term model optimization. It establishes a complete data chain encompassing latent state prediction, policy implementation, real-world effect feedback, error quantification and assessment, model parameter correction, and uncertainty parameter updates, forming a periodic automated iterative optimization mechanism. Continuously relying on the real-world effectiveness of policy implementation to drive model optimization, it continuously reduces latent state inference bias, allowing the model's state representation patterns to continuously adapt to the dynamic industrial environment, iterative policy rules, and enterprise growth characteristics, achieving a long-term and steady improvement in model inference accuracy, scenario generalization ability, and adaptability to complex operating conditions.

[0072] Step S180: Establish a data link across all modules, integrate various features to obtain the global state, and dynamically schedule each functional module according to the operating conditions, outputting features to the reinforcement learning module to realize policy decision-making and effect evaluation.

[0073] This embodiment achieves full-module data fusion and intelligent module dynamic scheduling, completing intelligent policy decision-making and effect evaluation output. The system connects the entire module data link, including feature extraction, state space construction, dynamic factor integration, abnormal data purification, environmental adaptive adaptation, latent state probability inference, and closed-loop iterative optimization. It integrates static growth features, dynamic environmental features, latent state features, uncertainty features, and corrected features to generate the enterprise's globally optimal state vector. Based on the real-time enterprise operating conditions and industry environment status, the system dynamically schedules the operating priority and weight ratio of each functional module, and stably inputs the final highly robust global state features into the reinforcement learning decision module, realizing the intelligent output of the entire process of precise policy matching, dynamic resource allocation, and intelligent evaluation of cultivation effects for enterprise gradient cultivation.

[0074] Based on the same inventive concept, please refer to Figure 2 This paper shows a schematic block diagram of a reinforcement learning-based high-value enterprise gradient cultivation policy precision delivery and effect evaluation system 100 provided in this application embodiment for executing the above-described reinforcement learning-based high-value enterprise gradient cultivation policy precision delivery and effect evaluation method. The reinforcement learning-based high-value enterprise gradient cultivation policy precision delivery and effect evaluation system 100 may include a communication unit 110, a machine-readable storage medium 120, and a processor 130.

[0075] In this embodiment, both the machine-readable storage medium 120 and the processor 130 are located within the reinforcement learning-based high-value enterprise gradient cultivation policy precision delivery and effect evaluation system 100 and are configured separately. However, it should be understood that the machine-readable storage medium 120 may also be independent of the reinforcement learning-based high-value enterprise gradient cultivation policy precision delivery and effect evaluation system 100 and may be accessed by the processor 130 via a bus interface. Alternatively, the machine-readable storage medium 120 may also be integrated into the processor 130 and may communicate and interact with external systems through the communication unit 110.

[0076] The processor 130 is the control center of the reinforcement learning-based high-value enterprise gradient cultivation policy precise targeting and effect evaluation system 100. It connects various parts of the system via various interfaces and lines, and executes software programs and / or modules stored in the machine-readable storage medium 120, as well as accessing data stored in the machine-readable storage medium 120. This allows for the execution of various functions and data processing within the reinforcement learning-based high-value enterprise gradient cultivation policy precise targeting and effect evaluation system 100, thereby providing overall monitoring of the system. Optionally, the processor 130 may include one or more processing cores; for example, it may integrate an application processor and a modem processor, where the application processor primarily handles the operating system, user interface, and applications, and the modem processor primarily handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. The machine-readable storage medium 120 is used to store machine-executable instructions for executing the scheme of this application, and the processor 130 is used to execute the machine-executable instructions stored in the machine-readable storage medium 120, so as to realize the method for precise delivery and effect evaluation of high-value enterprise gradient cultivation policy based on reinforcement learning provided in the aforementioned method embodiments.

[0077] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

[0078] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for precise deployment and effectiveness evaluation of high-value enterprise tiered cultivation policies based on reinforcement learning, characterized by: Includes the following steps: The system classifies and extracts features from multi-source heterogeneous data of enterprises. Corresponding feature learning algorithms are used to complete vectorized representation of structured time series data, unstructured text data, and graph data, and unified low-dimensional basic state features are obtained through attention fusion. A hierarchical composite state space is constructed, distinguishing between continuously changing feature dimensions and discrete level dimensions, setting state transition control logic, and adaptively adjusting feature weights based on the enterprise's development level. The system separates internal and external dynamic factors and integrates them into state representation, monitors data distribution shifts in real time, and identifies and suppresses interference from human-caused abnormal data on feature representation. We adopt a continuous learning and multi-model switching strategy to adapt to dynamic changes in the environment, and at the same time build a digital twin simulation environment to complete model pre-training under multiple working conditions based on the simulation scenario. A probabilistic inference model is established for the unobservable latent variables of the system. The distribution of the latent state is solved by combining time series observation data, and the uncertainty of the latent state inference is quantified. By combining three methods—information supplementation, confidence constraints, and knowledge transfer from similar samples—the observation data and latent state representation are improved, and subsequent decision-making behavior is constrained based on the inference confidence. Using the policy development cycle as a node, the implicit state inference model is corrected in reverse by using data on the implementation effect, forming a closed-loop iterative optimization mechanism; By connecting the data links of all modules and integrating various features to obtain the global state, and dynamically scheduling each functional module according to the operating conditions, the features are output to the reinforcement learning module to realize policy decision-making and effect evaluation.

2. The method for precise deployment and effect evaluation of high-value enterprise gradient cultivation policies based on reinforcement learning as described in claim 1, characterized in that: The construction of a hierarchical composite state space distinguishes between continuously changing feature dimensions and discrete level dimensions, sets up state transition control logic, and adaptively adjusts feature weights based on the enterprise's development level, including: The overall state space of enterprise development is divided into two independent and interconnected continuous growth subspaces and discrete hierarchical subspaces. Real number continuous vectors are used to model the continuous growth subspace, which carries the progressive changes in enterprise operation, R&D technology, talent structure, production capacity, revenue and profit. Within the discrete hierarchical subspace, three tiered labels are solidified: innovative enterprises, specialized and innovative enterprises, and specialized and innovative small giant enterprises. In conjunction with industry cultivation standards and historical sample data, a multi-dimensional hierarchical transition trigger threshold vector is set, which includes R&D investment intensity, number of patents, revenue growth rate, industrial chain supporting capabilities, and talent scale. Configure a hierarchical state gating unit to read the feature values ​​of the continuously growing subspace in real time, continuously compare the current feature vector with the hierarchical transition trigger threshold vector, and when multiple features continuously meet the threshold conditions and reach the preset stable period, trigger the gating unit to complete the discrete hierarchical label switching and hierarchical update. At the same time, embed the hierarchical offset feature vector in the global state vector to record the deviation data between the enterprise and the current hierarchical benchmark indicator and the adjacent hierarchical indicators. Construct a hierarchical feature weight mapping relationship, dynamically allocate feature weights according to the gradient level of the enterprise, increase the feature weights corresponding to revenue growth rate, capacity expansion, R&D investment growth rate and talent expansion speed for innovative enterprises, and increase the feature weights corresponding to technology patent barriers, irreplaceability of industrial chain, technology scarcity, R&D quality and operation stability for specialized and innovative enterprises and specialized and innovative small giant enterprises. Real-time synchronization of enterprise discrete hierarchical labels and updating of the weight coefficients of each feature dimension of the continuous growth subspace. Normalization processing is performed on the continuous growth subspace features, discrete hierarchical label features, and hierarchical offset features respectively. The three types of features are concatenated to obtain a composite state vector, thus completing the construction of the hierarchical composite state space.

3. The method for precise deployment and effect evaluation of high-value enterprise gradient cultivation policies based on reinforcement learning as described in claim 1, characterized in that: The decomposition system integrates internal and external dynamic factors into state representation, monitors data distribution shifts in real time, and identifies and suppresses interference from abnormal human data on feature representation, including: The disturbance factors of the enterprise gradient cultivation system are decoupled into internal and external binary categories, and a corresponding dynamic factor variable pool is constructed. The endogenous evolution disturbance of the enterprise and the exogenous disturbance of macro policies and industrial environment are modeled separately and integrated into the basic state vector of the enterprise as independent feature dimensions. A dual-index fusion online distribution shift detection mechanism is constructed. Based on the initial training distribution, the differences in online feature distribution are compared in real time and the degree of drift is quantified. The hierarchical threshold is used to realize the hierarchical identification and adaptation triggering of gradual environmental changes and structural abrupt changes. Establish a branch for identifying enterprise strategic data spoofing, and combine time-series mutation detection with cross-verification of multi-source objective data to identify abnormal indicators that are artificially modified or deliberately inflated by enterprises; An adaptive feature decay suppression mechanism is established to dynamically adjust the weight of abnormal features based on the degree of abnormal deviation, retain the normal operational fluctuation characteristics of the enterprise, and complete the adaptive filtering and feature correction of strategic noise. A multi-factor linkage state update mechanism is constructed, which integrates dynamic factor fluctuations, distribution drift quantification results, and anomaly correction information into the global state expression, and combines static and dynamic features to generate a highly robust enterprise state representation.

4. The method for precise deployment and effect evaluation of high-value enterprise gradient cultivation policies based on reinforcement learning as described in claim 3, characterized in that: The aforementioned branch for identifying enterprise strategic data manipulation combines time-series mutation detection with cross-verification of multi-source objective data to identify abnormal indicator characteristics of enterprises that are artificially modified or deliberately inflated, including: We selected performance indicators for enterprise tiered development, including revenue, R&D investment, number of patents, talent scale, and R&D equipment investment. We then conducted stationarity modeling on the historical time series of each indicator. By using first-order time series difference and second-order curvature change calculation methods, we captured the fluctuation range of the indicators periodically. We fitted the normal growth range and normal fluctuation threshold range of each indicator and identified step-like abrupt changes such as single-period jumps, cliff-like drops, and step-like mutations without continuous trend support. We marked abnormal indicator segments that deviated from the long-term growth pattern of the enterprise as suspected strategic modification data, thus initially distinguishing between normal and abnormal fluctuations in the enterprise's operations. Construct a multi-source objective data cross-verification link, establish a cross-source indicator verification mapping relationship between enterprise self-reported data and industrial chain knowledge graph data, tax invoicing data, social security participation data, patent publicity data, enterprise bidding and production capacity operation data, and for suspected abnormal indicators obtained from the time series initial screening, retrieve the corresponding multiple objective and authoritative data sources for true value comparison, and conduct cross-source cross-verification of enterprise reported R&D investment, number of talents, number of patent applications, and revenue growth data respectively. Based on the cross-source comparison results, abnormal data is determined. Indicator changes with significant deviations, supported only by data reported by the enterprise and without objective data to support them are determined to be strategically disguised data. Indicator changes with controllable fluctuation range and that can be effectively supported by multi-source objective data are determined to be the enterprise's real operational fluctuation data. A mechanism for locating and tracing the source of abnormal features and for confidence scoring is constructed to accurately identify the indicator dimensions, modification time points, and modification magnitudes corresponding to strategic modifications. By combining multiple factors such as the degree of temporal mutation, the magnitude of deviation in multi-source data, and the duration of anomalies, a comprehensive calculation is performed to generate a confidence score for indicator anomaly modification.

5. The method for precise deployment and effect evaluation of high-value enterprise gradient cultivation policies based on reinforcement learning as described in claim 1, characterized in that: The method employs continuous learning and multi-model switching strategies to adapt to dynamic environmental changes, while simultaneously constructing a digital twin simulation environment. Based on the simulation scenario, it completes model pre-training under multiple operating conditions, including: For scenarios with slow and gradual changes in the macro environment, an incremental continuous learning adaptation mechanism is constructed, and the general enterprise cultivation features and basic representation parameters of the basic model that have been trained are solidified and protected based on the elastic weight consolidation algorithm. After each policy development cycle ends, new enterprise operation data, industry environment data, and policy implementation data are collected. Only the branch network parameters that are dynamically adapted to the corresponding environment of the model are updated in small batches. While retaining the original general feature extraction capabilities of the model, it adapts to the gradual changes in data distribution brought about by industrial policy iteration, industry changes, and market fine-tuning. To address sudden environmental changes such as economic shocks, major policy revisions, and structural transformations in industries, a model branch hot-switching adaptation architecture is built. Based on multiple types of historical macroeconomic data sets, independent sub-model branches are pre-trained to adapt to different scenarios such as normal development, policy easing, policy tightening, economic downturn, and industrial restructuring. Each sub-model branch independently stores the network parameters and feature mapping rules corresponding to the scenario. Real-time collection of dynamic environmental factor data and input into the scene matching discriminator; through environmental factor feature similarity calculation, the optimal sub-model branch corresponding to the current real macroscopic working condition is matched, realizing rapid hot switching of model branches without running interruption or retraining, and adapting to the structural distribution drift of the environment. Based on the enterprise's full-cycle historical cultivation data, industry operation rules, policy implementation logic, and supply chain linkage mechanism, a digital twin simulation environment for enterprise tiered cultivation is constructed. This environment digitally replicates the endogenous evolutionary behavior of enterprise operation and growth, technology iteration, talent changes, and production capacity iteration, and simulates and restores the exogenous environmental changes scenarios such as fiscal adjustments, macroeconomic fluctuations, changes in industry competition, supply chain shocks, and regional support policy iterations. By configuring simulation parameters and disturbance thresholds, it supports simulation of single-variable disturbances and multi-variable combined disturbances, covering endogenous conditions such as normal enterprise growth, phased rapid development, strategic indicator modification, and short-term operational fluctuations, as well as exogenous conditions such as stable development, policy changes, economic shocks, and industry reshuffling. Based on the digital twin simulation environment, a large number of simulation samples are generated in batches under multiple enterprise development statuses, multiple environmental disturbances, and multiple policy conditions. The simulation samples under multiple working conditions are used to conduct pre-training of the model and learn the feature mapping rules under various extreme scenarios and rare working conditions. By aligning the rules and calibrating the parameters between the simulation environment and the real business scenario, the Sim2Real distribution gap between the simulation and the real scenario is reduced. Combined with incremental continuous learning and model branch hot switching mechanism, a comprehensive dynamic environment adaptation system is formed, which includes slow incremental adaptation, fast branch switching, and full-condition simulation pre-training.

6. The method for precise deployment and effect evaluation of high-value enterprise gradient cultivation policies based on reinforcement learning as described in claim 1, characterized in that: The process of establishing a probabilistic inference model for unobservable latent variables of the system, solving for the latent state distribution by combining time-series observation data, and quantifying the uncertainty of latent state inference includes: Construct a latent variable system for enterprise gradient cultivation scenarios, define latent factors that cannot be directly observed and collected, such as the enterprise's true technological potential, long-term growth potential, industrial resource adaptability, policy absorption and transformation capabilities, hidden operational risks, and future leap potential, and build a latent variable probability inference framework adapted to enterprise cultivation scenarios. Based on the observable time-series indicators related to enterprise operation, R&D, talent, production capacity, and industrial chain, a time-series probabilistic latent state inference model is built. An observation equation and a state transition equation are constructed. The mapping relationship between explicit observation indicators and the latent state inside the enterprise is established through the observation equation. The state transition equation describes the time-series evolution of the latent state with the changes of policy cycle, business cycle, and industrial environment. A temporal variational inference algorithm is used to process non-stationary, weakly periodic, and noisy enterprise cultivation time-series data. The time-series observation data is mapped to the latent space through an encoder, and the mean vector and variance vector corresponding to the latent state are output. The Gaussian posterior distribution of the latent state is solved to obtain the probability distribution interval of the enterprise's latent state at each time. A two-dimensional uncertainty quantification branch is constructed to calculate the cognitive uncertainty caused by insufficient model parameter fitting and lack of extreme working condition samples, as well as the random uncertainty caused by random disturbances in the industrial environment and short-term operational fluctuations of enterprises. The two types of uncertainty are then fused to obtain the overall uncertainty score for the latent state inference. A hidden state time series smoothing constraint mechanism is added. Based on the historical multi-period hidden state mean and evolution trend, the current hidden state distribution is regularized and corrected to suppress the hidden state distribution deviation caused by single-period abnormal data and ensure the continuity of hidden state time series evolution. The system outputs the probability distribution of the enterprise's hidden state, the optimal hidden state estimate, and the quantitative index of inferred uncertainty on a cycle-by-cycle basis, thus completing the probabilistic state characterization of the enterprise's hidden capabilities, potential risks, and growth potential.

7. The method for precise deployment and effect evaluation of high-value enterprise gradient cultivation policies based on reinforcement learning as described in claim 6, characterized in that: Based on observable time-series indicators related to enterprise operations, R&D, talent, production capacity, and industrial chain connections, a time-series probabilistic latent state inference model is constructed. This model establishes observation equations and state transition equations. The observation equations establish a mapping relationship between explicit observation indicators and the enterprise's internal latent state. The state transition equations characterize the time-series evolution of the latent state with policy cycles, business cycles, and changes in the industrial environment. This includes: Standardize and preprocess observable indicators in the dimensions of enterprise operation, R&D, talent, production capacity, and industrial chain linkage, unify the dimensions and data sampling period of each indicator, and organize them into a continuous multi-dimensional time series observation sequence according to the policy statistical cycle. Remove missing values ​​and outliers from the sequence to form a well-organized and time-aligned set of observation variables, which serves as the data source for model input. Complete the system state hierarchy division and variable definition, define the enterprise's technological potential, growth potential, resource adaptability, policy transformation capability, and implicit operational risks that cannot be directly collected and observed as implicit state variables, determine the dimension and value space of the implicit state vector, and achieve accurate division between observable variables and implicit state variables. Construct model observation equations, establish the correlation between multidimensional observation indicators at each time point and the hidden state vector of the same period through linear or nonlinear mapping functions, introduce observation noise terms that conform to the preset probability distribution, simulate random errors in the data collection, filling and statistical process, and establish a positive mapping relationship between explicit observation data and the hidden state of the enterprise. The model's state transition equation is constructed, and the hidden state time-series evolution function is built by combining the laws of enterprise operation, the pace of policy iteration and the characteristics of industrial environment evolution. The hidden state vector of the previous moment, the policy environment factor of the current period and the industry prosperity factor are used as inputs, and the hidden state vector of the current moment is output. State transition noise is added to represent the external environment disturbance and the uncertainty of enterprise development, and to depict the time-series dynamic evolution logic of the hidden state. Based on historical industry samples and benchmark enterprise data, the initial values ​​of the weight coefficients, bias terms, and noise distribution parameters of the observation equation and state transition equation were assigned. The maximum likelihood estimation method was used to substitute the segmented time series samples for iterative training and optimization, and the model mapping relationship and state evolution rules were corrected. After training, the model structure and all parameters are fixed, and real-time time-series observation data of enterprises are connected. At each time step, the current observation value and hidden state are associated through observation equations. At the same time, the current hidden state is deduced by combining historical hidden states and environmental variables through state transition equations, forming a closed-loop time-series probabilistic hidden state deduction link, which dynamically describes the evolution and changes of the enterprise's hidden state with policy cycles, business cycles, and industrial environment.

8. The method for precise deployment and effect evaluation of high-value enterprise gradient cultivation policies based on reinforcement learning as described in claim 6, characterized in that: The construction of a two-dimensional uncertainty quantification branch calculates the cognitive uncertainty caused by underfitting model parameters and missing extreme condition samples, as well as the random uncertainty caused by random disturbances in the industrial environment and short-term operational fluctuations of enterprises. The two types of uncertainty are then fused to obtain the overall uncertainty score for latent state inference, including: The uncertainty sources in the hidden state inference process are decomposed into dimensions, the calculation boundaries, influencing factors and data sources of cognitive uncertainty and random uncertainty are defined, and a two-dimensional parallel uncertainty quantification calculation branch is built to connect to model parameters, simulation samples, industrial environment time series data and enterprise operation data respectively. To quantify cognitive uncertainty, and address inference errors caused by model parameter fitting defects and missing extreme condition samples, multiple rounds of random sampling of model network parameters are performed during the model inference stage. Multiple hidden state inferences are completed using different parameter subsets, and the dispersion of multiple sets of hidden state inference results is statistically analyzed. A sample completeness correction coefficient is introduced by combining the proportion of extreme condition samples in the historical sample library. The cognitive uncertainty value is obtained by weighting the parameter perturbation deviation and the sample completeness correction coefficient. We conduct quantitative calculations of stochastic uncertainty, and extract contemporaneous industrial environment factors, supply chain data, and enterprise operating time-series indicators to address inference errors caused by random disturbances in the industrial environment and short-term operational fluctuations of enterprises. We calculate the variance and oscillation amplitude of multi-period indicators, and fit the interference intensity of external disturbances on the inference of hidden states by combining the noise distribution parameters of the hidden state transition equation. We then solve the inference bias corresponding to random disturbances through probabilistic statistical methods to obtain the numerical value of stochastic uncertainty. Based on the verification results of historical scenarios, the fusion weight coefficients of cognitive uncertainty and random uncertainty are calibrated, the two types of uncertainty values ​​are weighted and summed, and normalized and mapped to a unified numerical range to generate the overall uncertainty comprehensive score of the hidden state inference. The system presets uncertainty classification confidence intervals and matches corresponding high, medium, and low confidence levels based on the overall uncertainty score. After each round of hidden state inference is completed, it synchronously outputs the values ​​of cognitive uncertainty, random uncertainty, and overall uncertainty score. Continuously collect new working condition samples to supplement the sample library, dynamically update the sample completeness correction coefficient, iteratively weaken model cognitive bias, and build a closed-loop linkage mechanism for uncertainty quantitative assessment and continuous model optimization.

9. The method for precise deployment and effect evaluation of high-value enterprise gradient cultivation policies based on reinforcement learning as described in claim 1, characterized in that: The aforementioned closed-loop iterative optimization mechanism, which uses the policy cultivation cycle as a node and leverages implementation effect data to reverse-correct the latent state inference model, includes: Using the quarterly or annual policy implementation cycle of enterprise tiered cultivation as the iteration node, the system periodically backtracks and collects data on the actual implementation effects of the current round of policy investment, resource support amount, enterprise level leap results, revenue growth, patent increase, capacity improvement, and industrial chain support improvement. It constructs a periodic backtracking sample set corresponding to policy input and enterprise response effects, and matches and retains the latent state inference results and uncertainty scores from the early stage of the current cycle to form paired training samples of latent state prediction values ​​and actual cultivation effects. A quantitative evaluation system for hidden state inference bias is constructed. The hidden state parameters of enterprise technology potential, growth momentum, and policy absorption and transformation capacity predicted at the beginning of the cycle are compared with the actual leap performance, real growth increment, and policy implementation effect of enterprises at the end of the cycle. The single-cycle prediction bias, trend fitting error, and level matching error are calculated to quantify the degree of inference distortion of the model in this round and to locate the enterprise type, industry conditions, and policy scenarios corresponding to high bias. A multi-dimensional hierarchical error back propagation correction mechanism is established. Differentiated model parameter update strategies are set for different error sources. The existing training samples are supplemented with working conditions and the sample completeness coefficient and cognitive uncertainty parameter are iterated simultaneously. The evolution weight, environmental factor coefficient and noise distribution parameter of the state transition equation are fine-tuned in reverse, and the nonlinear mapping parameters of the observation equation are iteratively updated at the same time. Configure a multi-period iterative update constraint mechanism and set an error accumulation update threshold. Only when the same scenario generates the same inference deviation for multiple consecutive periods and the cumulative error exceeds the preset threshold, the model parameters are formally updated. The historical effective fitting parameters are retained, and the overfitting problem caused by the random fluctuation in a single period is avoided, so as to ensure the stability and authenticity of the model iterative optimization. We construct a fully automated closed-loop iterative chain encompassing latent state prediction, policy implementation, effect feedback, error assessment, parameter correction, and uncertainty updates. We continuously optimize the model by relying on the real effects of policy implementation, reducing the bias in latent state inference and enabling the model to dynamically adapt to the industrial environment and enterprise growth patterns.

10. A system for precise delivery and effectiveness evaluation of high-value enterprise tiered cultivation policies based on reinforcement learning, characterized in that: include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the method for precise delivery and effect evaluation of high-value enterprise gradient cultivation policy based on reinforcement learning, as described in any one of claims 1 to 9, by executing the machine-executable instructions.

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