Method for evaluating and predicting enterprise growth and ipo potential based on multi-source heterogeneous data and electronic device

Abstract

The present application relates to the field of data fusion evaluation, and provides an evaluation and prediction method for enterprise growth and IPO potential based on multi-source heterogeneous data, comprising: S11, collecting multi-source heterogeneous data and performing cleaning processing to generate a standardized enterprise analysis data set; S12, based on the enterprise analysis data set, extracting multi-dimensional enterprise features and constructing a comprehensive portrait library through clustering analysis, including enterprise individual features and plate common features; S4, taking the plate common features in the comprehensive portrait library as a reference, evaluating the enterprise individual features, and predicting the growth trend of the enterprise individual. Furthermore, the evaluation distortion caused by multi-source data fragmentation is solved, and a dynamic plate feature library is established to realize accurate enterprise evaluation.

Description

Technical Field

[0001] This invention relates to the field of data fusion assessment, specifically to a method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data. Background Technology

[0002] In the fields of financial investment, industrial planning, and corporate decision-making, a comprehensive assessment of a company's growth potential and IPO potential requires the integration of heterogeneous data from multiple sources, including business registration, finance, supply chain, and public opinion. However, this data is scattered across different systems (such as exchanges, government platforms, and media), exhibiting significant fragmentation: data silos are prominent, with a lack of unified identification and association for corporate business registration, public opinion, and supply chain data, resulting in a misattribution rate of approximately 35% for entities with the same name; implicit connections are difficult to uncover, and deep relationships such as the transmission of risks in the industrial chain and the linkage of market competition cannot be modeled due to data fragmentation; timeliness is lacking, and traditional batch processing is unable to respond to real-time events (such as the impact of sudden public opinion events). Here, traditional methods rely on "batch processing," which involves periodically (e.g., every early morning) extracting data from the database and running complex models for calculations. During the interval between two batch processing operations, the system is "blind" to new events and cannot respond.

[0003] The current solution has three major defects: (1) Data fusion layer: shallow associations lead to information fragmentation. Existing methods rely on rules or simple semantic alignment (such as name fuzzy matching), which is difficult to solve cross-source data ambiguity, only achieves surface splicing, and cannot build a deep association network. (2) Knowledge graph layer: static architecture cannot adapt to dynamic evolution. Existing knowledge graphs are mostly statically designed: entity relationships rely on manual predefinition, which makes it difficult to automatically capture new associations (such as new joint ventures); lack of confidence decay mechanism, which makes old relationships (such as terminated suppliers) continue to affect decision-making; implicit path analysis is limited to preset topology and cannot dynamically reconstruct multi-hop transmission chains (such as the transmission break of "raw material price increase → manufacturer cost increase → downstream profit decrease"), resulting in a misjudgment rate of 42% for industrial chain risks. (3) Predictive decision layer: fragmented associations lead to evaluation distortion. Enterprise profile construction is disconnected from real-time events; the predictive model does not incorporate dynamic weights of the knowledge graph and ignores the risk transmission of related entities; the inference results are disconnected from the underlying model and lack closed-loop optimization.

[0004] The aforementioned deficiencies all point to a core problem: fragmentation of multi-source data leads to the breakage of implicit connections between enterprise entities, and existing technologies lack a dynamic closed-loop mechanism to repair this. The fundamental issue lies in: insufficient cross-source disambiguation at the connection construction level and the inability to transform incremental events in real time; the knowledge graph at the connection evolution level not being coupled with dynamic business logic (such as supply chain changes); and the evaluation model at the connection application level not utilizing implicit transmission paths and the decision-making process not optimizing the network in reverse.

[0005] In summary, especially in the enterprise growth assessment stage, traditional methods, lacking a dynamically updated sector-specific reference system, are susceptible to interference from fragmented data in assessing individual enterprise characteristics. On the one hand, static industry averages fail to reflect real-time market changes, leading to the underestimation of high-growth enterprises. On the other hand, the disruption of implicit supply chain connections causes assessment models to overlook key risk transmission factors (such as the impact of upstream raw material fluctuations on downstream enterprises). Constructing a timely and comprehensive sector-specific characteristic library covering multiple dimensions of implicit connections as an assessment benchmark has become a key bottleneck in improving the accuracy of enterprise growth and IPO potential predictions. Furthermore, bridging the gaps in implicit connections between fragmented data and achieving dynamic closed-loop optimization are also unresolved technical challenges in this field. Summary of the Invention

[0006] Based on the above-mentioned shortcomings, one of the objectives of this invention is to overcome the evaluation distortion caused by the fragmentation of multi-source data and improve the accuracy of the evaluation of enterprise growth and IPO potential.

[0007] The second objective of this invention is to solve the technical problem of fragmented multi-source data in enterprises and the lack of implicit connections between the fragments.

[0008] In one embodiment of the present invention, a method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data is provided, comprising: S11 collects multi-source heterogeneous data and performs cleaning processing to generate standardized enterprise analysis datasets; S12 Based on the enterprise analysis dataset, extract multi-dimensional enterprise features and construct a comprehensive profile library through cluster analysis, including individual enterprise features and common features of sectors; S4. Using the common characteristics of the sectors in the comprehensive profile database as a reference, evaluate the individual characteristics of the enterprise and predict the growth trend of the individual enterprise.

[0009] Optionally, in some embodiments, step S12 further includes: The multi-source heterogeneous data is fused to generate fused data; Based on the fused data, a dynamic enterprise knowledge graph is constructed for the comprehensive profile database; Perform incremental clustering analysis to process the newly added enterprise data and recalculate the feature centroids of the sectors, generating a dynamically updated database of common features of the sectors; If the drift of the feature center point of the plate exceeds a preset threshold, the feature weight reassignment operation of the decision tree model is triggered to optimize the plate common feature evaluation system. By combining the entity relationship confidence decay mechanism of the dynamic enterprise knowledge graph, the timeliness status of outdated segment features is mapped to the evaluation weight decay factor, and the common features of segments after timeliness verification are output to the comprehensive profile library.

[0010] Optionally, in some embodiments, step S4 further includes: Based on the common features of the sectors in the comprehensive profile database, a multi-dimensional weighted similarity calculation model is constructed to calculate the weighted feature distance between the individual features of the enterprise and the common features of the sectors to generate an initial similarity value; Combining the entity relationship confidence decay mechanism of the dynamic enterprise knowledge graph, the matching path between the individual characteristics of the enterprise and the common characteristics of the sector is verified to see if it crosses entity relationships with confidence levels below a preset threshold, and a path reliability score is generated. The initial similarity value is corrected based on the path reliability score, and an enterprise individual characteristic assessment result with timeliness label is output; based on the enterprise individual characteristic assessment result, the growth trend of the enterprise individual is predicted.

[0011] Enterprise individual characteristic assessment is susceptible to the impact of fragmented data leading to broken connections, and traditional similarity calculations ignore the reliability of connection paths. This solution constructs a weighted feature distance model, quantifying the differences between individual enterprises and sectors through multi-dimensional indicators; it utilizes a knowledge graph confidence decay mechanism to verify matching paths and automatically identify distorted links caused by outdated relationships; it dynamically adjusts similarity values ​​based on path reliability scores, outputting assessment results with time-sensitive markers. Technical feature interaction manifests as follows: weighted calculation addresses the issue of differing indicator importance (e.g., patent weight is higher than administrative penalties), confidence verification severs invalid connections (e.g., negative transmission from terminated suppliers), and time-sensitive markers provide early warnings of the assessment result's validity period. These three elements work together to resolve assessment distortions caused by fragmented data, significantly reducing the misjudgment rate of enterprise growth potential.

[0012] Optionally, some embodiments of the method further include: A multivariate prediction model is trained based on historical data of the individual characteristics of the enterprise, and an initial prediction value is generated by dynamically adjusting the weight coefficients of revenue growth rate and net profit growth rate through a parameter optimization algorithm. Combining the entity relationship confidence decay mechanism of the dynamic enterprise knowledge graph, real-time incremental event features are injected to correct the initial prediction value, and the predicted distribution of financial indicators with time-sensitive labels is output. The visualization analysis engine maps the predicted distribution of the financial indicators into a dynamic deviation heatmap, marks the fluctuation range of key indicators, and feeds it back to the parameter optimization module of the multivariate prediction model.

[0013] Predicting enterprise growth trends is hampered by rigid models and a disconnect from real-time events. This solution employs a multivariate prediction model to dynamically adjust the weights of financial indicators, responding to industry cyclical changes. Incremental event features (such as policy changes or supply chain disruptions) are injected into a knowledge graph to correct predicted values ​​in real time. A visualization engine generates a deviation heatmap and feeds it back to the model parameter module, forming a self-optimizing closed loop. The parameter optimization module adjusts the weight coefficients in reverse based on the fluctuation range marked on the heatmap (such as the confidence band for revenue growth rate), enabling the model to continuously evolve. This closed-loop design overcomes the limitations of unidirectional prediction: dynamic weights address the issue of indicator importance drift, the event injection mechanism captures sudden influencing factors (such as raw material price fluctuations), and the feedback loop iteratively enhances prediction accuracy.

[0014] Optionally, some embodiments of the method further include: Based on the predicted growth trend of the individual enterprises, the pre-set quantitative indicators for listing in the securities market are matched to construct a timeline model for enterprise compliance and simulate the growth trajectory of key financial indicators. By combining the entity relationship confidence decay mechanism of the dynamic enterprise knowledge graph, the probability of the key financial indicators meeting the target in the time axis model is calculated and a listing time prediction value with confidence interval is generated. The visualization engine maps the predicted listing time to the corresponding probability of achieving the target as a heat map time axis, marks the key indicator gap data, and outputs it to the decision terminal.

[0015] Traditional IPO timeline prediction uses linear simulations, which cannot adapt to dynamic market changes. This solution introduces a reinforcement learning model to simulate the path to achieving targets, autonomously optimizing growth strategies through trial and error feedback (such as adjusting the balance between R&D investment and revenue); it combines knowledge graph confidence decay to calculate the dynamic probability of achieving key indicators, filtering out interference from outdated data; it generates a heatmap timeline to mark fluctuation thresholds, and feeds interactive decision signals back to the model. The interaction of technical elements is manifested in: reinforcement learning replacing fixed formulas (solving path failures caused by policy changes), confidence decay suppressing historical noise (such as the impact of outdated financial data), and interactive heatmaps enabling human-machine collaborative decision-making (e.g., investment banking experts manually adjusting expansion cycles). This solution upgrades IPO timeline prediction from static estimation to a dynamic evolutionary system. Tests show that the prediction error of the target achievement period is reduced, and the efficiency of identifying key bottleneck indicators is also improved.

[0016] Optionally, in some embodiments of the method, step S11 further includes: S1 performs fusion processing on the multi-source heterogeneous data to generate fused data; The steps and methods also include: S2. Based on the fused data, a dynamic enterprise knowledge graph is constructed for the comprehensive profile database to enable the real-time evolution of entity relationships between enterprise entities and multi-source heterogeneous enterprise data; and, S3 uses the dynamic enterprise knowledge graph to perform intelligent decision-making simulations and feeds the simulation results back to the dynamic enterprise knowledge graph to optimize its evolution.

[0017] Optionally, in some embodiments of the method, the sub-step of step S12, "performing incremental clustering analysis to process newly added enterprise data and recalculating the sector feature centroids to generate a dynamically updated sector common feature library," further includes: The system captures new enterprise data events in real time through a streaming data interface, extracts multi-dimensional feature vectors of the new enterprise data, and marks the data time stamp. The multi-dimensional feature vector is input into the incremental clustering engine, and the coordinates of the feature cluster center points are dynamically updated based on the enterprise dataset within the sliding time window. The Euclidean distance between the updated feature cluster center point coordinates and the historical center point coordinates is calculated as the plate feature center point drift. When the drift of the feature center point of the plate exceeds the dynamic threshold, the cluster radius adaptive shrinkage mechanism is triggered and the common feature library of the plate is updated.

[0018] Centroid drift detection in incremental clustering requires a balance between sensitivity and stability. This solution constrains the sliding window range with time stamps to ensure consistent data timeliness; it introduces industry distribution density weights when dynamically updating centroid coordinates to prevent the dilution of features from leading companies; it uses standardized vectors when calculating Euclidean distance to eliminate misjudgments caused by differences in dimensions; and it employs an adaptive shrinking mechanism for the cluster radius that is linked to the drift amount, with larger drift amounts resulting in stronger shrinkage. These four collaborative steps address key shortcomings of traditional incremental clustering: the sliding window prevents contamination from outdated data (e.g., only including companies updated within the last 90 days), weighted updates protect the representation of core company features (e.g., leading companies receive 60% weight), standardization ensures cross-industry comparability, and radius shrinkage strengthens the feature library's robustness (e.g., the cluster radius for emerging industries automatically expands by 20%). This solution enables the sector feature library to maintain high stability and representativeness under continuous data flow.

[0019] Optionally, the step of performing time-to-market prediction based on the enterprise growth trend forecast results further includes: The multi-source heterogeneous data is fused to generate fused data; Based on the fused data, a dynamic enterprise knowledge graph is constructed for the comprehensive profile database; A reinforcement learning model is constructed by integrating the quantitative indicators for listing with the predicted growth trends of the individual companies. The model dynamically optimizes the time to achieve revenue targets and the net profit growth strategy by simulating the trial and error feedback of the companies' achievement path. By combining the entity relationship confidence decay mechanism of the dynamic enterprise knowledge graph, the dynamic compliance probability of key financial indicators in the compliance path is calculated and a heat map of listing time distribution with confidence intervals is generated. The visualization decision engine maps the heatmap of the listing time distribution into an interactive time axis, marks the fluctuation threshold of the dynamic achievement probability, and feeds it back to the reinforcement learning model to optimize the strategy.

[0020] In the above steps, reinforcement learning in S1 provides the core of dynamic optimization, confidence decay in S2 ensures the authenticity and validity of the input data, and interactive feedback in S3 drives the continuous evolution of the system. The combined risks are addressed synergistically by these three elements, thereby reducing the time-to-market prediction bias.

[0021] Optionally, in some embodiments, step S12 further includes: S121. Generate a preliminary set of companies by integrating the lists of listed companies in the target sector provided by multiple data sources; S122. By cross-comparing the preliminary set of enterprises, determine the list of concept listed companies that meet the preset time period, and filter the list of target companies based on the principle of business relevance; S123. Identify common characteristics of the enterprises in the target enterprise list to generate common characteristics of the sector; S124. Based on the common characteristics of the aforementioned sectors, analyze the multi-dimensional analytical indicators of the target enterprise and integrate the analysis results into the comprehensive profile database.

[0022] In the above embodiments, the cross-validation of multi-source data and the strong correlation screening of business solve the technical problem of feature distortion caused by single data source or noise interference in the process of extracting common features of the segments.

[0023] Optionally, in some embodiments (3->2), step S3 further includes: S31) The inference results are fed back to the dynamic enterprise knowledge graph to trigger the update and confidence adjustment of the entity relationship, wherein the strength of the entity relationship and / or the confidence decreases over time; S32) Monitor the timeliness status of the entity relationship; S33) Dynamically adjust the confidence weight of the entity relationship based on the timeliness status, wherein the confidence of the old relationship is automatically decayed.

[0024] Optionally, in some embodiments (4->3), the method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data further includes: S34) Mark the timeliness of the inference results according to the decision timestamp; S35) triggers the confidence decay of outdated entity relationships in the dynamic enterprise knowledge graph.

[0025] The five steps S31-S35 are: acquiring dual-source enterprise data items, parsing the enterprise identification information, querying the identification mapping table, performing the logical matching calculation, and generating the enterprise entity association record. The technical solution comprised of these five steps effectively solves the technical problem of aligning multi-source heterogeneous enterprise data entities through the synergistic effect between these steps. Specifically: First, the data acquisition step S31 explicitly defines the first and second data sources as heterogeneous enterprise data sources (such as industrial and commercial and tax systems), which directly defines the scenario in which the technical problem arises: differences in data format and identification system between different systems prevent the automatic association of enterprise entities. Step S32 performs identification parsing on the dual-source data items, extracting key identification information (such as enterprise name and registration number), establishing a unified comparison benchmark for subsequent matching, and overcoming the processing obstacles caused by the unstructured or semi-structured nature of the original data.

[0026] Steps S33 and S34 constitute the core synergy of the matching logic. Step S33 achieves precise identifier matching through the preset mapping table (e.g., the mapping relationship between unified social credit code and tax registration number), applicable to scenarios where the coded identifier system is clearly defined. Step S34 achieves semantic alignment through association rules (e.g., fuzzy matching of enterprise names, address similarity calculation), covering situations where the identifier type is not defined in the mapping table or where data noise exists. These two steps complement each other to form the dual verification mechanism: when the preset relationship exists in the mapping table, the efficient matching is prioritized; otherwise, rule calculation is triggered, improving matching coverage while avoiding redundant computation.

[0027] Finally, step S35 generates the unified enterprise identifier or the associated record based on the matching result.

[0028] In summary, steps S31 and S32 locate the problem and standardize the input; steps S33 and S34 resolve the alignment of heterogeneous identifiers through the collaborative matching strategy; and step S35 outputs the structured association result. Thus, the entity alignment problem is solved, and a high-quality data foundation is provided for the assessment of enterprise growth and IPO potential.

[0029] Optionally, in some embodiments (5->4), step S2 further includes: S21. In response to real-time data stream events, new entity relationships are automatically established; S22. Based on the update of the multi-source heterogeneous enterprise data, dynamically reconstruct the implicit relationship paths between existing entities.

[0030] Here, "real-time data stream events" refer to incremental business data with time constraints that are continuously accessed through streaming interfaces, triggering the real-time evolution and path reconstruction of the dynamic knowledge graph. Its core characteristics include: Timeliness: The delay from data generation to access system is less than the threshold (e.g., ≤1 minute); Event-driven: The data includes business events that can alter entity relationships (such as supply chain disruptions or policy changes). Streaming input: continuous input via message queues (such as Kafka) or API streams, not batch import.

[0031] The two steps (or correspondingly referred to as Step 1 and Step 2) in the above embodiments constitute the feature extraction optimization scheme: establishing the feature extraction model based on the evaluation target and performing the feature importance screening. This scheme, through synergy with the entity alignment scheme in the above embodiments, effectively solves the technical problem of decreased evaluation accuracy caused by feature redundancy in multi-source data, specifically as follows: Step 1 constructs a dedicated feature extraction model for the evaluation of enterprise growth and IPO potential, thereby establishing a strong coupling between the model training objective and the evaluation task. Traditional feature extraction is usually based on general statistical indicators (such as mean and variance), while this step uses supervised learning to use the labels of enterprise growth and IPO potential as the training objective, enabling the model to learn deep feature representations highly correlated with the evaluation results. However, when this step operates alone, it still outputs some low-contribution features, increasing the downstream computational burden.

[0032] Step two achieves precise dimensionality reduction through importance screening. Based on the contribution ranking of the features to the evaluation target, a subset of key features (such as tax growth rate and patent activity) is dynamically retained, while redundant features (such as establishment year and number of employees) are eliminated. This step relies on the supervised training results of step one: without the target-oriented model, the importance of the features cannot be accurately quantified; without importance screening, the model output still contains noisy features. The two work together to form a closed-loop optimization mechanism, reducing the feature dimensionality while ensuring that the correlation between the retained features and the evaluation target exceeds a certain high threshold (e.g., 90%).

[0033] This scheme works in deep conjunction with the previous embodiments. The enterprise entity alignment in the previous embodiments ensures accurate association of the multi-source data (e.g., matching business penalties and tax data), providing high-quality input for step one; while the feature extraction results directly serve the evaluation model in this embodiment, forming the technical chain of "data alignment - feature optimization - evaluation prediction". Practical application shows that the selected feature subset improves the training efficiency of the evaluation model by 50%, and enhances the stability of the prediction results.

[0034] Optionally, in some embodiments, step S12 further includes: 1) Perform incremental clustering on the newly added enterprise data and recalculate the feature centroids of the sector; 2) Based on the drift of the feature center point, trigger the redistribution of feature weights in the decision tree; The method further includes: 3) using the common features of the modules in the comprehensive profile library as a reference, and combining the entity relationship confidence decay mechanism of the dynamic enterprise knowledge graph to perform enterprise individual growth trend prediction; The confidence decay mechanism further includes: 4) Map the time-related status of old plate characteristics to an evaluation weight decay factor; 5) When the matching path between individual characteristics and common characteristics of the module crosses entity relationships in the knowledge graph with confidence scores below the threshold, the reliability score of the evaluation path is automatically reduced.

[0035] Steps one through three achieve real-time risk perception, while steps four and five complete targeted model updates. This solution overcomes the inherent limitations of the static assessment model, providing sustainable and adaptive predictions of enterprise growth and IPO potential. This allows the solution to address the prediction inaccuracies caused by changes in the market environment in the static assessment model.

[0036] Optionally, in some embodiments (7->5), step S21 further includes: e) Import the real-time data stream events that reflect public opinion and incremental supply chain data in real time through the streaming interface; f) Trigger incremental updates to the dynamic enterprise knowledge graph in response to the incremental data via the real-time data stream events.

[0037] Optionally, in some embodiments (8->7), step S22 further includes: S5a) Extract weakly related entity pairs with confidence levels below a preset threshold from the dynamic enterprise knowledge graph and generate a set of paths to be reconstructed; S5b) Traverse the set of paths to be reconstructed, and inject cross-entity event association features based on the incremental public opinion and supply chain data in the real-time data stream events; S5c) Based on the coupling weights of the event association features and the inherent attributes of the entities, reconstruct the multi-hop transmission path between the weakly associated entity pairs; S5d) Based on the reconstructed multi-hop transmission path, update the transmission strength and timeliness marker of entity relationships in the dynamic enterprise knowledge graph.

[0038] The above four sub-steps S5a-S5d and their related (sub)steps form the closed loop of the dynamic reconstruction of implicit association paths, and the implicit association gaps between the fragmented data are filled through the reconstruction of the closed loop.

[0039] Optionally, in some embodiments, step S1 further includes: S1a) Perform semantic alignment on the multi-source heterogeneous enterprise data to generate a unified enterprise identifier; S1b) Based on the unified enterprise identifier, the associated business registration, public opinion and supply chain data are used to form the fused data.

[0040] Optionally, in some embodiments of the method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data, the method further includes: S1c) Perform semantic disambiguation on the business registration, public opinion, and supply chain data in the cross-source data of the multi-source heterogeneous enterprises to generate the unified enterprise identifier; S1d) Based on the unified enterprise identifier, the cross-source data associated with the same enterprise entity forms the fused data.

[0041] Optionally, in some embodiments of the method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data, step S3 further includes: S36) Extract the key impact paths of risk transmission or opportunity association in the dynamic enterprise knowledge graph; S37) Generate a visual topology map of the associated paths and label the risk probability or opportunity weight of the inference results; S38) Conduct risk transmission simulations among multiple entities along the key impact path, and perform multi-dimensional prediction and deduction of the business opportunities of the industrial chain to which the multiple entities belong.

[0042] Optionally, in some embodiments, steps S1, S2, and S3 respectively include: S1' integrates multi-source heterogeneous enterprise data and generates associative fused data through semantic alignment; S2' constructs an entity relationship network based on the fused data to achieve event-driven real-time evolution; S3' uses the entity relationship network to perform decision deduction and feeds the deduction results back to the entity relationship network to optimize its evolution.

[0043] In some embodiments, an electronic device for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data is also provided, comprising: a display; one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods of the plurality of methods in this application.

[0044] An apparatus for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data, according to some embodiments of this application, includes the following modules: (1) Data Integration and Cleaning Module: Data Acquisition: Collect multi-source heterogeneous enterprise data from multiple channels through various methods such as web crawlers, API interfaces, and data subscriptions, including but not limited to publicly available data from stock exchanges, enterprise annual reports, news information, industry research reports, and social media data. Data Cleaning: Clean and preprocess the collected multi-source heterogeneous data. Use data cleaning algorithms and tools to perform semantic alignment and semantic disambiguation, including removing duplicate data, handling missing values, correcting erroneous data, unifying data formats, and generating unified enterprise identifiers, ultimately generating a standardized enterprise analysis dataset. Data Storage: Construct a distributed data storage system, such as the Hadoop Distributed File System (HDFS) or cloud storage services, to store the cleaned and standardized massive heterogeneous data. At the same time, use relational databases (such as MySQL and Oracle) and non-relational databases (such as MongoDB and Redis) to classify and store structured and semi-structured data to meet the storage and query needs of different types of data.

[0045] (2) Enterprise Profile Construction Module This module aims to build a comprehensive profile library based on the enterprise analysis dataset. Feature Extraction: First, data mining, natural language processing, and other technologies are used to extract multi-dimensional enterprise features that constitute the profile from the standardized dataset. These features cover multiple dimensions such as basic enterprise information, financial status, products and services, innovation capabilities, and industry links. Profile Modeling: Then, machine learning algorithms (such as cluster analysis, decision trees, neural networks, etc.) are used to model the extracted features. Through cluster analysis, not only are individual enterprise features formed, but also common features of the sector are extracted, which together constitute a dynamically updated comprehensive profile library. The model scores and comprehensively evaluates the features of each enterprise to form an intuitive and visual enterprise profile.

[0046] (3) Feature matching algorithm for potential enterprise matching module: Based on the common features of sectors in the comprehensive profile database, a feature matching algorithm is designed. The weighted feature distance between the individual features of an enterprise and the common features of the sector is calculated to generate a similarity value. Enterprise ranking and screening: Enterprises are ranked according to their feature similarity and growth score. Based on actual needs, the top-ranked enterprises with the greatest development potential are screened to provide a reference for investors and enterprise decision-makers.

[0047] (4) Enterprise Growth Trend Prediction Module Model Selection and Training: Based on the characteristics of the enterprise's historical data and prediction needs, select a suitable prediction model, such as a multivariate prediction model or a regression analysis model. Train the model using historical data and dynamically adjust the weight coefficients through parameter optimization algorithms to improve prediction accuracy. Trend Prediction and Result Analysis: Apply the trained model to predict the enterprise's future growth trend, predicting key financial indicators such as revenue and profit. Analyze and interpret the prediction results, output the predicted distribution of financial indicators with time-sensitive labels, and evaluate the reliability and rationality of the prediction results.

[0048] (5) Listing Time Prediction Module: Listing Standard Review: Collect and organize the quantitative listing indicator requirements of various securities markets, including regulations on revenue scale, net profit level, market capitalization requirements, etc. Time Prediction Model Construction: Combine the enterprise growth trend prediction results and the quantitative listing indicator requirements to construct an enterprise compliance timeline model. By simulating the enterprise growth process, calculate the compliance probability of key financial indicators in the timeline model, and predict the time required for the enterprise to reach the listing standards. Prediction Result Output and Visualization: Output the listing time prediction results in intuitive charts and reports through a visualization engine, generate listing time prediction values ​​with confidence intervals, and provide reference for enterprise listing planning and investor investment decisions.

[0049] In addition, in some embodiments of the present invention, the following alternative or alternative solutions may also be adopted: (6) Modification of data acquisition method In addition to automated data collection methods such as web crawlers, API interfaces, and data subscriptions, some embodiments may also consider establishing cooperative relationships with data providers to directly purchase high-quality, pre-processed multi-source heterogeneous data services, or import incremental data in real time through streaming interfaces to improve the efficiency, coverage, and quality of data collection. (7) Model algorithm variation example: In some implementations, the machine learning algorithms or statistical models used in modules such as enterprise profiling, enterprise growth trend prediction, and time-to-market prediction can be replaced or combined. For example, in enterprise profiling, in addition to algorithms such as clustering analysis, decision trees, and neural networks, algorithms such as support vector machines (SVM) and random forests can also be used; in enterprise growth trend prediction, in addition to the multivariate prediction models and regression analysis models already mentioned, models such as convolutional neural networks (CNN) or generative adversarial networks (GAN) in deep learning can also be considered; in time-to-market prediction, in addition to the target timeline model, reinforcement learning models can be used for path simulation and optimization. The selection and replacement of these models can work in conjunction with mechanisms such as dynamic enterprise knowledge graphs and confidence decay to improve the overall system performance and adaptability.

[0050] [Technical Effects]: Through methods in several embodiments, the dynamic knowledge graph and the incremental clustering analysis work in synergy: the clustering analysis not only extracts the static features but also processes newly added enterprise data through the incremental update mechanism, recalculating the sector feature centroids to ensure the dynamic adaptability of the sector common feature library. When the drift of the sector feature centroids exceeds a preset threshold, the feature weights of the decision tree model are reassigned to optimize the evaluation system. Simultaneously, combined with the entity relationship confidence decay mechanism of the knowledge graph, the evaluation weights of outdated sector features are automatically reduced, avoiding interference from outdated data on the prediction results. This dynamic adjustment mechanism enables the enterprise growth assessment to adapt to market fluctuations; for example, when raw material prices fluctuate drastically, the system can quickly adjust the weights of supply chain-related features to ensure prediction accuracy.

[0051] In other embodiments, in step S1, the data ambiguity is eliminated, providing the raw materials for the latent association mining; then, in step S2, the raw materials are transformed into the dynamic relationship network, and in other preferred embodiments, the association is optimized in real time; in step S3, the network application results are fed back to the system starting point, driving the continuous closure of the latent association gaps. This forms a closed loop, rather than a unidirectional process. By synergistically filling the latent associations between the fragmented data, the aforementioned technical problems are also solved. Attached Figure Description

[0052] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1This is a flowchart illustrating a method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data, according to an embodiment of the present invention. Figure 2 This is a block diagram illustrating an assessment and prediction device for enterprise growth and IPO potential based on multi-source heterogeneous data, according to an exemplary embodiment. Figure 3 This is a flowchart illustrating a method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data, according to an embodiment of the present invention. Detailed Implementation

[0053] Through extensive research, the inventors discovered statistically significant connections between individual companies and their respective industry sectors, which can be manifested in at least the following specific aspects: Common characteristics of listed companies across different sectors: Through specific sector division methods, data aggregation methods, and algorithms such as statistical analysis and association rule mining, the common characteristics of listed companies in each sector are accurately extracted, providing a scientific basis for matching potential companies.

[0054] Multi-dimensional enterprise growth assessment system and model; Enterprise growth trend and listing time prediction model; A complete enterprise analysis and IPO potential prediction process: The complete process proposed in this invention, from data integration and cleaning to IPO time prediction, involves close cooperation and coordination among the modules to form an organic whole. Based on the design and implementation of this relatively complete process, the following embodiments are proposed to implement the corresponding technical solutions of this application.

[0055] In one embodiment of this application, a method for assessing and predicting the growth potential and IPO potential of enterprises in the rail transit and artificial intelligence fields based on multi-source heterogeneous data is proposed, including steps S11, S12, and S4, as follows: Figure 3 As shown: Step S11 performs data acquisition and cleaning, and outputs a standardized dataset to step S12; Step S12 constructs a common feature library of the blocks through incremental clustering. When the drift amount Δ of the feature center point exceeds the threshold, the decision tree weight redistribution sub-process is triggered to form a closed loop optimization. Step S4 receives the feature library output and executes it sequentially: Twenty companies (10 each in the rail transit and artificial intelligence fields) were initially selected based on common standards. Apply similarity calculation to calculate model similarity Six key targets with a similarity greater than 85% were selected. Output the predicted and verified time to market. This flowchart fully covers the S11-S12-S4 technology chain of claim 1, highlights the differentiated processing in the fields of rail transit and artificial intelligence (government order weight 0.65 vs. patent weight 0.6), and reflects the dynamic feedback mechanism (Δ>threshold triggers weight redistribution).

[0056] In the fields of rail transit and artificial intelligence, there are two major pain points in assessing the listing potential of traditional enterprises: Fragmented data makes it impossible to accurately quantify the average growth trend of the industry, and in particular, it lacks the ability to extract the key development patterns of successful listed companies. The screening of unlisted companies relies on subjective experience and ignores the reference value of common characteristics of the sector for growth paths, resulting in a failure rate of over 40% for high-growth companies.

[0057] In this embodiment, a solution is proposed that executes the above three steps sequentially. This method can be applied to the two high-growth sectors of rail transit and artificial intelligence. Specifically: In step S11, multi-source heterogeneous data acquisition and cleaning are performed.

[0058] Data source coverage: Rail Transit: Ministry of Industry and Information Technology's Equipment Manufacturing Enterprise Database, High-speed Rail Bidding Platform, Patent Database (Patents related to traction systems and intelligent dispatching); Artificial Intelligence: Ministry of Science and Technology's AI Enterprise Directory, Academic Paper Repository (CV / NLP Field), Investment and Financing Platform (Companies at Series A and Above); Cleaning rules: Unify corporate identity (e.g., "CRRC XX subsidiary" is merged into "CRRC Group"); Complete missing financial fields (e.g., interpolate the R&D expense ratio using the industry average). Standardized growth rate metrics (revenue growth rate = compound annual growth rate (CAGR), net profit growth rate = audited annual report data).

[0059] In step S12, a common feature library of the plates is constructed. Extracting common standards of successfully listed companies (using companies that have passed the review for listing on the Science and Technology Innovation Board / ChiNext as a sample): Incremental clustering optimization: Newly listed companies are dynamically included (e.g., 12 new AI companies were added in 2023), and the center point of the sector's characteristics is recalculated. When the center point of the AI ​​sector's profit growth rate changes from 28% to 32% (Δ=0.13>threshold 0.1), a redistribution of decision tree weights is triggered, and the weight of algorithm patents increases from 0.4 to 0.6.

[0060] In step S14, individual enterprise growth trends are predicted and screened. Initial screening of 20 high-potential companies (meeting common criteria for the sector): Ten companies in the rail transit sector: such as "Intelligent Rail Control" company (government orders account for 35%, CAGR=19%). Ten companies in the field of artificial intelligence: such as "DeepVision Technology" (23 CV patents, CAGR=27%). Precisely identify key targets: Using the average growth trend of the sector as a baseline, calculate the similarity of individual characteristics: Wᵢ: Weight of the i-th feature, Fᵢ, Individual: Value of the target enterprise on the i-th feature, Fᵢ, Sector: Average value of the sector on the i-th feature, n: Total number of features.

[0061] Six companies with a similarity greater than 85% were selected, and their IPO prospects are predicted to be met within two years. Rail Transit: "HuaDun Signal" (88% similarity) - Government orders are only 5% short, and it is expected to be listed in 24 months; Artificial Intelligence: "Language-based Interaction" (91% similarity) - Patent conversion rate exceeds the sector average, predicted to go public in 18 months.

[0062] This solution has achieved certain technical effects in the following aspects: Accuracy of Quantifying Growth Trends: The common feature library of the sector dynamically reflects industry patterns (such as the threshold for the profit growth rate of AI companies changing from 25% to 32%), which reduces the false screening rate of potential companies by 37%. Individual assessments incorporate growth rate deviation weighting (w) i For rail transit companies, the "order growth rate" has a weight of 0.7, which avoids interference from low-value indicators.

[0063] Improved scene adaptability: Rail transit companies rely on policy guidance, and the weight of "government orders" among their common characteristics has increased to 0.65. Artificial intelligence companies focus on technological barriers, with patent indicators accounting for over 50% of similarity calculations.

[0064] IPO forecasting efficiency: Of the 20 companies initially screened, all 6 key candidates submitted IPO applications within 24 months (100% approval rate), which is 3 times faster than the traditional model.

[0065]

Example 1

[0066] Step S2's dynamic evolution mechanism addresses the lack of implicit connections: Based on the fused data from S1, step S2 constructs an event-driven dynamic knowledge graph. It automatically establishes new connections through real-time data stream responses (such as supply chain changes) and couples with confidence decay mechanisms from other embodiments to eliminate outdated relationships (e.g., zeroing the weight of terminated supplier relationships). This design enables the real-time capture of implicit connections such as supply chain risk transmission (e.g., path update delays are reduced to 15 minutes). Simultaneously, path reconstruction sub-steps S5a-S5d from other embodiments further optimize multi-hop transmission chains. This step transforms the data fusion results from step S1 into a dynamic connection network, helping to address the deficiency of "difficulty in uncovering implicit connections."

[0067] The closed-loop feedback in step S3 enables system self-reinforcement: Step S3 utilizes the dynamic graph from step S2 for intelligent extrapolation (such as multi-hop risk propagation simulation) and feeds the extrapolation results back to the graph optimization evolution. This closed-loop design creates key efficiency gains: The inference bias can also trigger implicit path reconstruction in other embodiments (such as injecting incremental event features in step S5b) to continuously repair missing associations; the optimized graph improves the accuracy of subsequent decisions (e.g., improves the accuracy of assessing corporate growth and IPO potential), and thus enhances timeliness through the decay mechanism in some other embodiments.

[0068] The process of step 3 ensures that the outputs of steps S1-S2 are continuously calibrated, ultimately resolving the problem of "the evaluation model being disconnected from the underlying data".

[0069] This can be understood as follows: Entity relationships refer to the directed weighted edges in a Dynamic Enterprise Knowledge Graph that connect different enterprise entities or entities and events. These edges quantify the association attributes, influence strength, and timeliness of the relationships between entities. Mathematically, they are defined as triples. Where: ":=" means "defined as", R entity Entity relationship, also known as the relationship between (different) entities; E source For the source entity (such as lithium ore supplier CN_LITH_008), E target For the target entity (such as battery company CN_BATT_001) W rel The Relationship Weight Vector contains the following core properties: 1. Conduction strength Icond∈[0,1]: Characterizes the influence on the effectiveness of the conduction, such as the strength of a purchase contract I.cond =0.92 2. Confidence level C∈[0,1]: dynamically updated based on the timeliness score St (see formula St=α⋅exp(−t / τ)+β⋅Revent) 3. Expiration Date Tvalid: Relationship validity period; expiration triggers decay. Features that distinguish it from traditional concepts 1. Dynamic Evolution In response to real-time data stream events (such as policy changes detected in step S21), new relationships or reconstructed paths are automatically generated (such as adding a new edge for "policy risk transmission").

[0070] 2. Controllable Decay The weight decay rate is controlled by the timeliness scoring model St (e.g., when the old procurement relationship St=0.19, the intensity drops to the initial value of 19%).

[0071] 3. Multi-hop Conduction It supports cross-entity indirect association modeling (such as the path [policy risk → supplier → target company → car company]), breaking through the limitations of explicit direct association.

[0072] Application scenario examples In the evaluation of new energy vehicle battery companies, typical entity relationships include: Those skilled in the art should understand that the entity relationship in this application is not a static attribute, but rather a combination of timeliness weight St (step S32) and event relevance R. event The dynamic variables in step S5b have their weight updates driven by a closed-loop feedback mechanism (error in step S31 triggers graph update in step S5d). This design solves the evaluation distortion problem caused by rigid relationships in traditional knowledge graphs, improving the accuracy of capturing the transmission path of hidden risks in the industrial chain to 89%.

[0073]

Example 2

[0074] Scenario description of "Risk Transmission Assessment of Supply Chain of Companies Planning to Go Public" A securities firm is assessing the listing potential of a new energy vehicle battery manufacturer (the target company), and needs to anticipate the risk of being impacted by both fluctuations in upstream lithium ore prices and changes in downstream automaker orders. Data sources include fragmented and heterogeneous data such as the target company's business registration information, upstream lithium ore supplier contract data, downstream automaker public opinion data, and industry policy updates.

[0075] Step S1 Implementation Details: Semantic Fusion of Multi-Source Heterogeneous Data Data collection and cleaning: 1) Scrape the business registration information (including subsidiary level) of lithium mining suppliers, the financial audit reports of target companies for the past three years, and the public opinion on capacity adjustment of downstream car companies on social media; 2) Clean redundant fields (such as duplicate supplier historical names) and fill in missing contract expiration dates.

[0076] Semantic alignment and disambiguation Establish unified identification rules: 1) Business registration data uses the "Unified Social Credit Code" as the core identifier; 2) Supply chain data is linked to the actual controlling enterprise through "contract filing number + equity penetration of the contracting entity"; 3) Downstream car companies' public opinion is disambiguated by "company abbreviation + main product category" (for example, "Car Company A" is normalized as "New Energy Company A" in public opinion).

[0077] Output: The target enterprise's integrated data label is CN_BATT_001, associated with upstream supplier CN_LITH_008 and downstream customer CN_AUTO_015.

[0078] Step S2 Implementation Details: Dynamic Knowledge Graph Construction and Evolution Initial map construction Establish entity relationships with CN_BATT_001 as the central node: For example: Upstream relationship: [CN_BATT_001]-[Raw Material Procurement]->[CN_LITH_008] (strength weight 0.9); Downstream relationship: [CN_BATT_001]-[Supply Agreement]->[CN_AUTO_015] (strength weight 0.8).

[0079] Real-time evolution is triggered, for example, by events such as: Event 1: Detected public opinion regarding sudden policy changes in lithium ore exporting countries (e.g., accessed via streaming interfaces in other embodiments). Automatically create a new relationship: [CN_LITH_008] - [Policy Risk Transmission] -> [CN_BATT_001] (Initial confidence level 0.7); Event 2: Downstream automaker CN_AUTO_015 reportedly reduced orders by 30%; Dynamically adjusted relationships: The downstream supply agreement strength weight is reduced from 0.8 to 0.5, triggering confidence decay in some other embodiments.

[0080] Step S3 Implementation Details: Intelligent Decision-Making and Closed-Loop Feedback Risk Transmission Simulation Based on graph-based multi-hop path analysis: Policy risks (CN_LITH_008) → Rising raw material costs (CN_BATT_001) → Decreased supply capacity (CN_AUTO_015); Projection results: The target company's gross profit margin forecast for the next three quarters is revised downward by 15%.

[0081] Feedback Optimization Map An inference was found: the actual gross profit margin only decreased by 8% (due to the target company's emergency switch to an alternative supplier). This can also trigger path reconstruction in some other preferred examples (steps S5a-S5d): For example, in step S5a, weakly related entity pairs (CN_BATT_001, CN_LITH_008) are extracted; in step S5b, the incremental event feature "alternate supplier contract signing" is injected; then in step S5c, the multi-hop path [CN_LITH_008]-[risk buffer]->[CN_BATT_001] is reconstructed; finally, in step S5d: the original procurement relationship strength weight is updated to 0.6, and an alternative supplier relationship is added (weight 0.7).

[0082] Therefore, by re-analyzing the optimized graph, the gross profit margin decline was revised to 7.5% (the error rate was reduced from 46% to 6%), making the target company's risk resistance better than expected, and the estimated listing time was 6 months ahead of schedule.

[0083] The application of the technical solution consisting of steps S1-S3 in this application in this scenario illustrates the synergistic effect of steps S1-S3: The data fusion in step S1 ensures the accuracy of entity association (such as equity penetration of backup suppliers); while the dynamic evolution in step S2 captures policy and order events in real time and establishes a risk transmission chain; then, through the closed-loop feedback in step S3, the graph is reconstructed through inference deviations to continuously correct the lack of implicit associations (such as risk buffer paths), and ultimately improve the reliability of listing assessment.

[0084] Those skilled in the art should understand that the meanings of several technical terms, such as "implicit association path," in the above embodiments can be understood in conjunction with the following aspects: Dynamic Enterprise Knowledge Graph: A relational network architecture driven by real-time data streams, which models enterprise entities (such as suppliers and customers), events (such as policy adjustments), and attributes (such as financial indicators) as nodes and edges, supporting the automatic evolution of entity relationships and confidence decay.

[0085] Weakly related entity pairs: Relationships in a knowledge graph with a confidence level below a preset threshold (e.g., 0.6) indicate that their reliability is insufficient due to outdated data or changes in events.

[0086] The transmission of hidden risks: Many major risks faced by enterprises do not come directly, but are transmitted through a series of indirect links.

[0089] Conversely, there is also a potential for interconnected opportunities: similarly, positive opportunities can be transmitted through implicit paths. For example, a policy that benefits upstream technologies (such as breakthroughs in new energy battery technology) (Event C) → stimulates the development of related component manufacturers (Company E) → enhances the competitiveness of end-product manufacturers using this technology (Company F) → ultimately benefits Company F's partners or investors (Company G). Identifying the path (C → Technology → E → F → G) helps to discover potential opportunities in advance.

[0090] Therefore, traditional static analysis models or basic knowledge graphs often pre-determine fixed association rules, making it difficult to automatically discover and dynamically update these complex, multi-layered entity (enterprise, event, industry, etc.) implicit chains that depend on specific contexts (such as emergencies). They cannot "see" the invisible, dynamically changing connections between data fragments.

[0091] Entity Network Based on Knowledge Graph: The core of the patent is the construction of a "dynamic enterprise knowledge graph." This graph uses various enterprise entities (target companies, suppliers, customers, competitors, etc.), events (policies, public opinion), and attributes (financial indicators, industry) as "points" (nodes), and connects them with various relationships (supply relationships, competitive relationships, influence relationships, event triggering relationships, etc.) as "lines" (edges). Implicit association paths are the indirect transmission chains in this complex entity relationship network graph that require multiple "points" and "lines" to connect.

[0092] One of the core innovations of this application is the reconstruction algorithm's ability to dynamically discover, construct, and update these hidden paths. Its mechanism includes: Locating weaknesses: First, identify which relationship links (edges) in the knowledge graph have become unreliable due to outdated data or low confidence ("weakly related entity pairs"). These places may be the key points where implicit paths are broken or distorted.

[0093] Injecting event features: When new real-time events (such as news of supply chain disruptions) occur, the system will transform this event information into features that can describe the relationship (such as "scope of impact" and "severity of the event") and inject them into the relevant weak areas of the knowledge graph.

[0094] Implicit linking paths refer to indirect relationship chains between corporate entities that are not directly apparent but have a substantial impact on corporate development or risk transmission. These paths connect seemingly isolated entities or events into a network of influence with causal logic by traversing multiple intermediate links (such as secondary partners of upstream suppliers, market behavior of competing companies, and the supply chain impact of policy events).

[0095] The differences between implicit and traditional correlation paths include the following aspects: 1) Non-explicit: They cannot be directly obtained through explicit data such as business registration, equity, and contract texts (e.g., the pressure on downstream automakers' costs due to policy adjustments in lithium exporting countries needs to be transmitted through three levels: "policy → miners → battery manufacturers → automakers"). 2) Dynamic: They rely on real-time event-driven generation (e.g., sudden public opinion triggers the restructuring of supply chain alternative paths). 3) Multi-hop: They cross at least two intermediate nodes (e.g., competitor's technological breakthrough → increased industry financing threshold → target company's R&D is hindered).

[0096] Traditional enterprise assessments are limited by data silos, only able to analyze direct correlations (such as contract purchase volume), resulting in a hidden risk coverage rate of less than 50%. This patent makes implicit correlation paths explicit through dynamic knowledge graphs and event feature injection: for example, in the case of new energy vehicle batteries, it captures the cross-level transmission chain of "graphite purification patent → anode cost reduction → battery gross profit increase"; another example is that the reconstruction path coverage rate is increased to 92%, and the risk warning time is advanced by 37 days.

[0097] Therefore, implicit correlation paths can help solve the industry pain point of "fragmented data leading to the break of implicit causal relationships" and provide full-chain insights for the assessment of enterprise growth and IPO potential.

[0098]

Example 3

[0099] During step S11, multi-source heterogeneous data cleaning and standardization are performed: Business registration information of the target battery company (CN_BATT_001), contract data of the upstream lithium mine supplier CN_LITH_008, and public opinion reports and industry policy updates from the downstream automaker CN_AUTO_015 are collected. A unified identification rule is established through semantic disambiguation: business registration data uses the unified social credit code as the core identifier; supply chain data is linked to entities through "contract registration number + equity penetration"; and public opinion data is normalized using "company abbreviation + product category" (e.g., "A New Energy" is classified as CN_AUTO_015).

[0100] During the data cleaning process, missing contract expiration date fields were filled in, and missing values ​​in financial data were filled in using interpolation algorithms, reducing the entity association error rate from the industry average of 35% to 5.2%. Those skilled in the art should understand that the standardized dataset output by this step provides an unambiguous data foundation for subsequent feature extraction, especially resolving the semantic ambiguity in cross-source data where "a certain technology company" may refer to a listed entity, subsidiary, or shell company.

[0101] In addition, in step S12, multidimensional features are extracted based on the standardized dataset, including individual enterprise features (such as CN_BATT_001's R&D investment ratio of 12% and capacity utilization rate of 85%) and common features of the sector (such as the average gross profit margin of the new energy battery sector of 28%).

[0102] Incremental clustering is used to dynamically update feature centroids: when a public opinion event of "lithium ore export tariff increase of 10%" is detected, the sector risk index centroid is recalculated, triggering feature drift detection. The drift amount Δ = ||C|| new -C old When Δ > threshold θ (assuming θ = 0.1), the decision tree feature weight redistribution is initiated, and the weight of "raw material cost sensitivity" is changed from w i =0.6 to 0.8. Preferably, this process uses a coupling time decay factor α=exp(-λΔt) (λ=0.01, Δt is the number of days the data is updated), so that the impact of outdated data decays exponentially, ensuring that the sector feature library reflects market changes in real time.

[0103] In step S4, using the dynamically updated common characteristics of the sectors as a reference, the matching degree of individual characteristics of CN_BATT_001 is evaluated: the weighted matching degree between the individual and the sector is calculated as M = 1 - ∑|w i ·(F i Individual-F i ,sector)| / ∑w i , where w iThese are the feature weights adjusted in step S12. When a matching path crosses an outdated relationship in the knowledge graph with a confidence score <0.6 (e.g., CN_LITH_008, whose confidence score drops to 0.58 due to 90 days of no data updates), an automatic decay mechanism is triggered, reducing the reliability score of that path by 30%. By coupling real-time public opinion event features (e.g., "CN_AUTO_015 orders reduced by 30%), a revised growth forecast is output: CN_BATT_001's gross profit margin will decrease by 7.5% in the next three quarters, a 18.5% reduction in error compared to the traditional model forecast (9.2%) without the decay mechanism.

[0104]

Example 4

[0105] Application Scenarios Background A securities firm is assessing the listing potential of a target company (CN_BATT_001, a new energy vehicle battery manufacturer) and needs to anticipate the risk of a two-way transmission from fluctuations in upstream lithium mine prices to reduced orders from downstream automakers. Data sources include: Business registration data: Equity structure of CN_BATT_001 and upstream lithium mining supplier CN_LITH_008 Supply chain data: Supply agreements between CN_BATT_001 and downstream automaker CN_AUTO_015 Public opinion data: CN_AUTO_015 production capacity adjustment dynamics disclosed on social media Policy Data: News of Sudden Changes in Trade Policies of Lithium-Exporting Countries Detailed Explanation of the Implementation of the Technical Solution In step S1, multi-source heterogeneous enterprise data is collected and fused to generate fused data. Specifically, in step S1a: The business registration information (including subsidiary levels) of CN_LITH_008, the financial audit reports for the past three years of CN_BATT_001, and the public opinion on capacity adjustments of CN_AUTO_015 are crawled. Entity identifiers are normalized using semantic disambiguation rules. - Business registration data uses the "Unified Social Credit Code" as its core identifier (e.g., 91440300MA5FK). - Supply chain data is linked to the actual controlling enterprise through "contract registration number + equity penetration of the contracting entity" (e.g., HT2023_AC108). - Public opinion data uses "company abbreviation + main product category" for disambiguation (e.g., "Company A" also known as "New Energy Vehicle A" is normalized to CN_AUTO_015). In step S1b, fused data tags are generated based on the unified identifier associated data: -CN_BATT_001 is associated with upstream CN_LITH_008 (raw material procurement strength 0.92) and downstream CN_AUTO_015 (supply agreement strength 0.88). - Output a structured fusion dataset, reducing the entity association error rate from the industry average of 35% to approximately 14.8%. Data fusion verification: By identifying CN_LITH_008's backup supplier CN_LITH_012 (formerly an unrelated entity) through equity penetration, a supplementary supply chain risk buffer path is provided.

[0106] Step S2: Construction and Evolution of Dynamic Knowledge Graph In step S21, the following event occurs: Event 1: Monitoring public opinion regarding sudden policy changes in lithium ore exporting countries (real-time data stream event) - Automatically create a new relationship: [CN_LITH_008]-[Policy Risk Transmission]->[CN_BATT_001] (Initial confidence level 0.85) -Event 2: Public opinion regarding CN_AUTO_015 order reduction of 30% - Dynamically adjust relationship strength: The specific implementation of step S22 includes: Step S5a is triggered: Extract the weakly related entity pair (CN_BATT_001, CN_LITH_008) (confidence 0.58 < threshold 0.6) Step S5b execution: Inject incremental event characteristic: "CN_BATT_001 enters into an emergency procurement contract with backup supplier CN_LITH_012" Step S5c Reconstruction: Calculate the coupling weights between event features and financial attributes: Reconstruct the multi-hop path: [CN_LITH_008] - [Risk Buffer] -> [CN_BATT_001] Step S5d includes the following update implementation: - Added new relationship [CN_BATT_001] - [Alternate Procurement] -> [CN_LITH_012] (Strength 0.75) - Update the original procurement relationship strength to 0.65 Confidence decay mechanism: The weights of outdated relationships decay exponentially: As a result, the weight of data that has not been updated for 90 days is reduced to below the initial value of 31%.

[0107] Step S3: Intelligent Decision-Making and Feedback Optimization Step S36 is implemented: The key impact paths are extracted as follows: Policy risks (CN_LITH_008) → Rising raw material costs (CN_BATT_001) → Decreased supply capacity (CN_AUTO_015) Implementation of step S38: - Input variables: - Multi-hop propagation model: Implementation of step S31 feedback process: The actual gross profit margin is 30.5%, with errors calculated as follows: Specifically, the map optimization process is triggered when the error δ > 5%.

[0108] -Step S5a-Step S5d Reconstruct the path, adding a backup supplier buffer effect. - Step S33: Update confidence level: The weight of the policy risk transmission path decays to 0.62. Secondary deduction verification for step S38: The revised gross margin forecast is 30.2% (error reduced to 1.0%). The implementation of the above steps has yielded significant results in the following aspects: 1. The effectiveness of implicit association path reconstruction By injecting event features and calculating coupling weights in steps S5b-S5c, the implicit transmission chain of "lithium mining policy → alternative suppliers → target company gross profit margin" (not covered by traditional models) is identified.

[0109] Quantitative improvement: The risk coverage rate of the industrial chain increased from 61% to 94%, and the misjudgment rate decreased by 37%.

[0110] 2. Accuracy of dynamic confidence decay Step S33 adjusts the weights based on the timeliness status, so that the interference weight of outdated relationships (such as terminated suppliers) decays to below 0.1 within 3 months.

[0111] Quantitative improvements: Gross margin forecast error has been reduced from the industry average of 12.3% to 1.8%, and the time to market forecast deviation has been reduced from ±9 months to ±1.8 months.

[0112] 3. Closed-loop optimization efficiency The simulation results feedback (step S31) and map update (step S5d) form a closed loop, shortening the risk transmission simulation iteration cycle from 14 days to 4 hours.

[0113] Quantitative improvement: The target company's risk resistance assessment score improved from 6.2 to 8.5 (out of 10), which will help bring forward the estimated listing time by 6 months.

[0114] Simulation tests conducted by a securities firm show that, using the above method, the accuracy of predicting enterprise growth trends improves by approximately 20.2%, the false screening rate for potential enterprises decreases by approximately 19.7%, and the error range for estimating listing time narrows from ±9.2 months to ±4.8 months. In the above embodiments, this technical solution achieves the following in the evaluation of new energy vehicle battery manufacturing enterprises through a collaborative mechanism of multi-source data semantic fusion (step S1) → dynamic graph evolution (step S2) → decision feedback closed loop (step S3): Real-time capture of hidden risk paths in the industrial chain (such as the multi-hop transmission of policies → raw materials → battery costs → automaker orders); Based on confidence decay ( Event-driven dynamic weight optimization enables the prediction model to continuously self-calibrate. The assessment of listing potential has been expanded from a single financial indicator to the ability to resist risks through industry chain synergy, and the gross profit margin prediction error has been reduced to 19.8% (compared to more than 36% for traditional models).

[0115]

Example 5

[0116] Those skilled in the art should understand that this step quantifies the staleness of relationships using the timeliness scoring model T_s=F_data×R_event (where F_data is the data freshness function and R_event is the event relevance), thus avoiding the waste of resources from full graph traversal. Tests show that this mechanism reduces redundant computation by approximately 68%.

[0117] In step S5b, incremental public opinion events E1 = "Sudden policy change in lithium exporting countries" and supply chain events E2 = "CN_AUTO_015 Order reduction of 30%" are accessed in real time via a streaming interface and transformed into event-related feature vectors. For weakly related entity pairs in Γ, the injected feature dimensions include: policy impact level L. p =2 (Country → Enterprise), Order Impact Strength I o =0.75. It should be noted that the event characteristics and the inherent attributes of the entity (such as the financial elasticity coefficient β=0.6 of CN_BATT_001) form a multi-dimensional feature space, providing cross-source correlation clues for path reconstruction.

[0118] During the execution of step S5c, multi-hop propagation path reconstruction was implemented: based on the coupling weight W of event features and entity attributes. c =α·I_event+(1-α)·β (α=0.7 is the event weight coefficient), calculate the probability of path reconstruction. When W c When the threshold η = 0.65, a multi-hop propagation path P is generated. new [CN_LITH_008]-[Risk Transmission]->[CN_BATT_001]-[Order Response]->[CN_AUTO_015].

[0119] Preferably, this step breaks through the limitations of traditional static graphs by coupling weights to determine the number of path hops. For example, when an emergency procurement event of backup supplier CN_LITH_012 is detected, a new buffer path [CN_LITH_008]-[risk buffer]->[CN_BATT_001] is added.

[0120] During the execution of step S33, dynamic confidence decay was implemented. Specifically, for the reconstructed transmission path, a time-driven weight decay model was established: Relationship Confidence C t =C_0×exp(-λ·Δt), where the attenuation coefficient λ=0.015 and Δt is the number of days without update. Due to Δt=90 days, the confidence level of the original procurement relationship attenuates from the initial C0=0.9 to 0.41; the initial C0 of the newly added backup supplier relationship is 0.85. After normalization, the weight of the backup path increases to 67%, accurately reflecting the dominant position of emergency procurement.

[0121] During the execution of step S5d, real-time evolution of the knowledge graph was achieved. Specifically, the knowledge graph topology was updated as follows: 1) A new [risk buffer] relationship was added and marked with a time stamp; 2) The strength of the original procurement relationship was reduced from 0.9 to 0.65; 3) The transmission strength index γ=min(1,∑W was updated based on the multi-hop path reconstruction results. c ).

[0122] In summary, through the synergy of S5b event feature injection and S5c coupling weights, the system automatically reconstructs the implicit transmission chain of "policy risk → alternative suppliers → capacity buffer" in lithium mining policy change events. This path is omitted in traditional static models, resulting in an actual gross profit margin prediction error of 7.8%, while this solution reduces it to 1.2%. The closed-loop effect of the S33 decay mechanism and S5d graph evolution causes the weights of outdated supply chain relationships to decay by 52% within 60 days, while the transmission strength γ of the new path is increased to 0.78, and the model iteration cycle is shortened from 96 hours to 4 hours.

[0123] Some industry tests conducted by the inventors show that the solution increases the risk coverage of the industrial chain from 71% to 92%, and reduces the estimated deviation of the time to market for enterprises from ±8.3 months to ±6.4 months.

[0124]

Example 6

[0125] Step S31) Implementation process of "feeding the inference results to the dynamic enterprise knowledge graph to trigger the update of entity relationships and adjustment of confidence": Initial simulation (time T0): Based on knowledge graph path policy risk (CN_LITH_008) → increased costs (CN_BATT_001) → decreased gross profit margin Output forecast: A 22% increase in lithium ore prices will cause CN_BATT_001's gross profit margin to decrease from 35% to 28%. Actual data feedback (time T1, 3 months later): The actual gross profit margin was 30.5% (2.5% higher than the forecast). Root cause: Failure to capture the emergency procurement agreement between CN_BATT_001 and backup supplier CN_LITH_012 Feedback triggering mechanism: Calculation of inference error rate: The map update is automatically triggered when δ > 5% (the threshold is set according to industry benchmarks). Error feedback enables the system to automatically identify missing risk buffer paths, thereby converting projection deviations into map optimization instructions without manual intervention. S32) Implementation process for monitoring the timeliness status of entity relationships: Construction of the time-sensitive state matrix: 1. Timeliness scoring model: Among them, F data Data source freshness (an inverse function of time elapsed since the last time), R event Recent related event matching degree Although the relationship CN_LITH_008→CN_BATT_001 is facing expiration due to outdated data (not updated for 90 days), the timeliness status visualization through steps S32 and other steps in this embodiment improves the efficiency of identifying outdated relationships.

[0126] Step S33) Implementation process of "Dynamically adjusting the confidence weight of entity relationships based on timeliness, wherein the confidence of outdated relationships automatically decays": 1. Attenuation model execution: The confidence level of the old relation CN_LITH_008 → CN_BATT_001 decreases: The new relation CN_BATT_001→CN_LITH_012 has an initial confidence level of C0=0.8. 2. Weight redistribution: Total weight of raw material procurement path = Cprimary supplier + Calternate supplier = 0.41 + 0.8 = 1.21 Normalization adjustment: The primary supplier's weight Wm = 0.41 / 1.21 = 0.34, and the backup supplier's weight Wb = 0.8 / 1.21 = 0.66. This step automatically shifts the weights towards real-time data. Furthermore, the backup supplier path accounts for 66% of the weight, accurately reflecting the dominant role of emergency procurement.

[0127] S22) Dynamically reconstruct implicit related paths (cooperative steps S31-S33) Implementation process of steps S5b-S5d: 1. Step S5b Event Feature Injection: Incremental public opinion events: CN_AUTO_015 announced it will distribute its battery orders to multiple suppliers, while CN_BATT_001's market share has dropped to 50%. Extracting the event feature vector: E = [Decrease in customer concentration, increase in competition intensity, increase in price sensitivity] 2. Step S5c Multi-hop Path Reconstruction: Coupling weight calculation: Among them, E impact =0.8, representing the intensity of the event's impact; FinStability=0.6, representing the target company's cash flow health. This leads to a new path: Order dispersion (CN_AUTO_015) → Increased competition → Gross margin pressure (CN_BATT_001) 3. Step S5d map update: The relationship strength of CN_BATT_001→CN_AUTO_015 was reduced to 0.4. The initial confidence value for the new path is C0 = 0.85. The above technical solution achieves the following technical effects: 1. The attenuation model overcomes the static bottleneck. In traditional models, stale supply chain relationships continue to influence decisions (e.g., the weight of terminated suppliers is still calculated at 0.7). However, in this solution, the exponential decay model of S33 automatically reduces the weight of relationships not updated for 90 days to 0.41. Therefore, in the CN_AUTO_015 order dispersion event, the system automatically reduces the weight of original customer dependencies, accurately captures changes in the competitive landscape, and improves the accuracy of predicting the impact of order reductions. 2. Event-driven refactoring of implicit paths Traditional methods only reveal explicit supply chain relationships (such as contract filing data). This solution, however, injects public opinion event characteristics into S5b to construct a transmission chain of "order dispersion → market competition → gross profit margin." Therefore, it can provide early warning of CN_BATT_001's market share risk (earlier than the financial report release cycle). 3. Closed-loop feedback iteration efficiency In the traditional process, manual verification followed by model adjustment takes 14 days. However, in this solution, step S31, error-triggered automatic reconstruction, is completed within 4 hours. Therefore, in the event of a sudden change in lithium mining policy, ideally, the system can reduce the prediction error from 8.2% to 1.8% within two iterations.

Example 7

[0128] Specifically, incremental events are captured in real time through a streaming interface: 1) Public opinion event E1 = "Trade ban by lithium ore exporting countries" (feature vector: policy intensity I) p =0.92, impact level L=3); 2) Supply chain event E2 = "Downstream car company CN_AUTO_015 order cancellation 40%" (default coefficient β=0.78). In response to event E1, the dynamic knowledge graph automatically generates a new entity relationship: CNLITH008-⟨policy risk transmission>→CNBATT001, initial confidence level C0=0.85×min(1,I p ×L / 3)=0.79.

[0129] Those skilled in the art should understand that this process quantifies the association strength through the event feature weighting function f(E)=∑wᵢ·Fᵢ (wᵢ is the feature weight, Fᵢ is the normalized feature value), and compared with traditional timed batch processing, the relationship establishment delay is shortened from 6 hours to 9 minutes.

[0130] The purpose of step S22 is to achieve: dynamic reconstruction of implicit paths. Specifically, based on event E2, implicit association analysis is triggered as follows: 1) Extract weakly associated entity pairs Γ={(CN_BATT_001,CN_AUTO_015)} (the confidence level of the original supply agreement drops to 0.52 due to data lag); 2) Inject the event feature "order cancellation ripple index" δ=β×γ (γ=0.65 is the supply chain dependence coefficient); 3) Calculate the attribute coupling weight W. c =α·δ+(1-α)·μ (α=0.6 is the event impact factor, μ=0.7 is the CN_BATT_001 cash flow health). When W c When the threshold η=0.62, the multi-hop transmission path is reconstructed as follows: Raw material cost pressure → Production capacity compression → CNBATT001 → Delivery risk → CNAUTO015.

[0131] It should be noted that this approach reveals a "second-order transmission effect" that traditional methods have failed to capture, reducing the gross profit margin prediction error from 9.1% to 2.3%.

[0132] Finally, in step S33, the confidence level of time-driven factors is decayed.

[0133] Specifically, a dual attenuation mechanism is established for the reconstruction path: 1) Inherent attenuation according to C t =C0×e^{-λΔt} (λ=0.012, Δt is the number of days without update), the policy transmission relationship decays to 0.68 due to Δt=45 days; 2) Event decay is based on C e =C t×[1-log(1+ν)] (ν is the number of related event conflicts), when the "CN_AUTO_015 switch to alternative supplier" event is detected, the confidence level of the delivery risk path is further reduced by 22%. After normalization, the weight of the newly added transmission path reaches 61%, accurately reflecting the changes in the risk pattern.

[0134] Those skilled in the art should understand that some concepts in the above embodiments can be interpreted in the following ways: Streaming interface: refers to the software and hardware modules that support continuous access to real-time data streams. Through the event parsing engine, it transforms unstructured information (such as public opinion texts and supply chain anomalies) into structured feature vectors. Its feature dimensions include event type, scope of impact, entity correlation, etc., providing incremental data sources for dynamic knowledge graphs.

[0135] Incremental update: A mechanism that optimizes only local subgraphs related to events in real time while maintaining the stability of the main structure of the knowledge graph. Its core is to realize the addition, deletion and modification of entity relationships through event feature binding and coupled calculation, thereby avoiding the resource consumption of full graph reconstruction.

[0136]

Example 8

[0137] Those skilled in the art should understand that such multi-source heterogeneous data suffers from fragmentation, delayed updates, and broken implicit correlations, making it difficult for traditional methods to accurately assess a company's growth potential. The following section details the implementation process and collaborative mechanism of the technical solution, comprising 10 selected sub-steps.

[0138] Detailed steps: 1. The data fusion stage in the method for assessing and predicting enterprise growth and IPO potential using multi-source heterogeneous data in this embodiment: semantic alignment and identifier association (steps S1a-S1b) Specifically, in step S1a, semantic disambiguation is performed on the multi-source data: Business registration data uses the unified social credit code as the core identifier (e.g., 91440300MA5FK). Supply chain data is linked to the actual controlling enterprise through "contract filing number + equity penetration" (e.g., HT2023_AC108→CN_LITH_008). Public opinion data is normalized using "company abbreviation + product category" (e.g., "A New Energy" is mapped to CN_AUTO_015).

[0139] In step S1b, fused data labels are generated based on the unified identifier: Output a structured dataset and associate CN_BATT_001→CN_LITH_008 (raw material procurement strength 0.92) and CN_BATT_001→CN_AUTO_015 (supply agreement strength 0.88).

[0140] It should be noted that semantic alignment reduces the cross-source entity association error rate from the industry average of 35% to below 5%, providing unambiguous input for dynamic knowledge graphs.

[0141] The multi-source heterogeneous data mentioned here refers to enterprise data with dispersed sources and different structures, such as business registration information (structured), public opinion texts (unstructured), and supply chain records (semi-structured). The core challenge lies in the semantic consistency and real-time relevance of cross-source data.

[0142] 2. The event response and path reconstruction stage (steps S21, S5a-S5d) in the multi-source heterogeneous data enterprise growth and IPO potential assessment and prediction method of this embodiment. Specifically, step S21 captures events in real time through a streaming interface: Event E1: Monitoring public opinion regarding sudden policy changes in lithium ore exporting countries (Policy Intensity I) p =0.92, impact level L=3). Event E2: Downstream automaker CN_AUTO_015 orders reduced by 30% (default coefficient β=0.78).

[0143] In response to E1, the system automatically establishes a new relationship: CN_LITH_008→Policy Risk Transmission→CN_BATT_001, Initial Confidence Level C0=0.85×min(1,I p ×L / 3)=0.79.

[0144] In step S5a, the weakly related entity pair (CN_BATT_001, CN_LITH_008) is extracted (the original procurement relationship has not been updated for 90 days, so the confidence level is C). t =0.58 < threshold θ=0.6).

[0145] Step S5b injection event characteristics: Calculate the order cancellation ripple index δ=β×γ (γ=0.65 is the supply chain dependence coefficient); Generate feature vectors [δ=0.51, L=3].

[0146] Step S5c reconstructs multi-hop paths: Coupling weight W c =α·δ+(1-α)·μ (α=0.6 is the event factor, μ=0.7 is the health of the company's cash flow); When W c When η=0.62, the generation path is: policy risk → raw material cost → CN_BATT_001 capacity reduction → CN_AUTO_015 delivery risk.

[0147] Step S5d updates the graph: The new path conduction intensity γ=min(1,∑W c ) = 0.75; The strength of the original procurement relationship decreased from 0.9 to 0.65.

[0148] The streaming interface here refers to a software and hardware module that supports real-time data access, which transforms unstructured events (such as public opinion texts) into structured feature vectors, reducing latency from the traditional 6 hours to minutes.

[0149] 3. The decision deduction and feedback optimization stage (steps S38, S31, S33) in the multi-source heterogeneous data enterprise growth and IPO potential assessment and prediction method of this embodiment. In step S38, risk transmission simulation is performed along the reconstruction path: 1) Input variables: Lithium ore price increase ΔP = 22%, capacity utilization rate U = 85%; 2) Multi-hop transmission model output: CN_BATT_001 The gross profit margin forecast for the next 3 quarters is Ĝ=28% (actual value G=30.5%, error δ=8.2%).

[0150] Step S31 triggers feedback optimization: When δ>5%, the system automatically identifies the missing backup supplier CN_LITH_012 and adds a new buffer path CN_LITH_008→risk buffer→CN_BATT_001.

[0151] Step S33: Dynamically decaying confidence level: 1) Weighting of outdated relationships is based on C t =C0×exp(-λΔt) (λ=0.012, Δt=45 days) decays to 0.68; 2) The initial weight of the new path is C0=0.8, and the weight ratio increases to 66% after normalization.

[0152] Preferably, closed-loop feedback shortens the model iteration cycle from 14 days to 4-9 hours and reduces the gross profit margin prediction error from 8.2% to 4.1%-1.8%.

[0153] Through the coordination of the steps in the above embodiments, a precise profile of the enterprise is achieved: through the semantic fusion of S1a-S1b, the error rate of enterprise entity association is reduced to 5%, solving the problem of "data silos"; In addition, the characteristics of the sectors in which the relevant companies are located were also extracted: the multi-hop path reconstructed in step S5c reveals the implicit transmission chain of "policy → raw materials → battery cost → car orders", and the coverage rate has increased from 64% to about 87%. Furthermore, the growth potential of the target company is also assessed: the confidence decay mechanism in step S33 reduces the interference of outdated data by 50%, and the gross profit margin prediction error is better than the industry average (±3% vs. ±8%). [Example 9] Predicted Scenario for New Energy Vehicle Battery Companies to List on the Science and Technology Innovation Board The data foundation involved in the above prediction scenarios includes: Company growth trend forecast: Revenue growth rate of 12.3% and net profit margin of 8.7% for the next three years. Quantitative indicators for listing: For companies listed on the Science and Technology Innovation Board, a market capitalization of ≥4 billion yuan and revenue of ≥300 million yuan in the most recent year are required. Dynamic knowledge graph: includes entity relationships such as supplier CN_LITH_008 and customer CN_AUTO_015. Step S1: Reinforcement Learning Model Construction and Dynamic Optimization By integrating market capitalization, revenue, and net profit indicators of companies listed on the Science and Technology Innovation Board (STAR ​​Market) with enterprise growth forecast data, a reinforcement learning model is constructed. The model simulates 10,000 paths to success using Monte Carlo tree search, implementing a trial-and-error feedback mechanism in each simulation round. Initial strategy: Prioritize expanding production capacity to increase revenue (weight ω) rev =0.7,ω profi t=0.3) Feedback Adjustment: When the raw material cost increase in the simulation path exceeds a threshold (e.g., lithium price increase > 15%), the revenue weight is automatically reduced to ω. rev =0.5 and increase the net profit weight to ω profit =0.5 Optimized output path: The time to achieve the target is shortened from the initial estimate of 28 months to 24 months, and the compound annual growth rate of net profit is increased to 10.2%. Those skilled in the art should understand that this model overcomes the limitation of traditional linear programming in responding to price fluctuations by dynamically balancing the objective (α=0.6, β=0.4) through the reward function R=α⋅(ΔRevenue)+β⋅(ΔProfit).

[0154] Step S2: Confidence decay and heatmap generation By combining the entity relationship confidence decay mechanism of dynamic knowledge graphs, key indicators in the achievement path are dynamically calibrated: Decay Execution: The raw material cost data for supplier CN_LITH_008 has not been updated for 60 days, and the confidence level has decayed from C0=0.9 to C. t =0.9×e^(-0.012×60)=0.48 Calculation of probability of achieving target: Update the revenue target probability based on the decayed weights. P rev =Σ(w i P i (w) i For the attenuated weights, P i (Probability of sub-indices) The probability of achieving revenue targets decreased from 82% to 67%, and the probability of achieving net profit targets decreased from 75% to 63%. 3. Heatmap Output: Generates a time-probability distribution heatmap. The red area (probability <65%) marks the period from Q4 2023 to Q1 2024 as a high-risk period. It should be noted that the confidence interval of the heatmap reflects the real-time evolution of the knowledge graph (e.g., policy risks may cause the interval width to reach ±18%). Step S3: Interactive Decision-Making Loop Map the heatmap to an interactive timeline using a visualization engine: Fluctuation threshold labeling: Quarters with a net profit target probability of less than 60% (Q4 2023) are automatically marked as red alert zones. Manual intervention: Investment banking experts manually increased the weighting of R&D investment to ω rd =0.4 (originally 0.3), triggering model re-optimization Closed-loop feedback: The optimized strategy (postponing capacity expansion and increasing solid-state battery R&D) is fed back to the knowledge graph to update the entity relationship weights (e.g., the relationship strength of "R&D investment - patent output" is increased by 0.2). After re-optimization, the probability of achieving the net profit target in Q4 2023 has rebounded to 68%, and the estimated listing time has been revised to 25 months. It should be understood that the terms used here can be interpreted as follows: Reinforcement Learning Model: A machine learning framework that autonomously optimizes decisions through a trial-and-error mechanism, used in this application to simulate the path to achieving a company's IPO goals. Its core components include a state space (company financial indicators), an action space (strategy adjustment), and a reward function (proximity to the IPO goal), achieving strategy evolution through a dynamic interactive environment.

[0155] Confidence Decay Mechanism: A mathematical model that reduces the weight of entity relationships based on data timeliness; the calculation formula is C. t =C0×exp(-λ·Δt), where λ is the decay coefficient and Δt is the number of days the data has not been updated. This mechanism addresses the prediction distortion caused by outdated dynamic data such as supply chain and public opinion data.

[0156] Interactive Timeline: Transforms time-dimensional data into a visual decision-making interface that allows for human intervention, supporting functions such as parameter adjustment, risk labeling, and model feedback. Its innovative value lies in breaking down the barriers between predictive models and human decision-making, forming a two-way optimization loop.

[0157] Dynamic path optimization overcomes the limitations of static models When the lithium ore tariff was increased by 20%, the traditional linear model, due to its fixed parameters, could not adjust the revenue / net profit weights, resulting in a prediction error of 32%. Step S1 of this solution uses a reinforcement learning trial-and-error mechanism to generate an emergency strategy (e.g., increasing the proportion of procurement from backup suppliers to 40%) within 4 hours after the event, keeping the time deviation for achieving revenue targets within 8%.

[0158] Attenuation mechanism eliminates data timeliness distortion The target company's original supply chain data update cycle was 90 days, resulting in a cost prediction distortion rate as high as 28%. After introducing confidence decay in step S2, the weight of outdated data dropped to below 0.5. Combined with real-time public opinion injection (e.g., the cooperation event with new supplier CN_LITH_012), the accuracy of calculating the probability of net profit reaching the target was improved to 91%.

[0159] Human-machine closed-loop accelerates decision-making iteration Through the interactive timeline of step S3, investment banking experts identified the risk of a net profit shortfall in Q4 2023 and manually adjusted the R&D investment weights to trigger model re-optimization. This operation shortened the strategy iteration cycle from 14 days to 6 hours, reduced resource mismatch rate by 40%, and ultimately compressed the IPO preparation cycle to 25 months (5 months shorter than the industry average).

[0160] The three-step technology loop demonstrates significant advantages in the face of sudden policy changes in the lithium mining industry: S1 dynamically generates alternative paths using a model, S2 uses a decay mechanism to filter out data from failed suppliers, and S3 provides a visual interface that feeds back risk thresholds to the model. The synergy of these three elements reduces time-to-market prediction deviation from ±9 months to ±2.3 months, improves key bottleneck identification efficiency by 3 times, and provides highly adaptable go-to-market planning tools for technology-intensive enterprises.

[0161]

Example 10

[0162] 2. Incremental clustering engine update (corresponding to steps 5 and S2) Dynamically perform incremental clustering based on a sliding time window (window size = 4 quarters): 2.1 Assigning weights ω to patent data of leading companies i =1.2 (SMEs ω) i =0.8) 2.2 Calculate the new coordinates of the plate feature center point: C new =(∑(ω i F i ) / ∑ω i (F) i (For patent technology feature vectors) 2.3 When event E2 is detected, the weight of solid-state battery-related patents is temporarily increased to 1.5, shifting the center point towards high energy density technology.

[0163] Optionally, the window sliding step size is 1 quarter to ensure the continuity of feature evolution.

[0164] 3. Drift amount drives radius adaptation (corresponding to steps S3 and S4) 3.1 Calculation of Drift Amount Comparison with historical center point C old (2023Q2 coordinates), calculate the standardized Euclidean distance: Δ=‖C new -C old || / σ (σ is the characteristic standard deviation) The measured Δ = 0.17 (threshold θ = 0.15).

[0165] 3.2 Cluster radius shrinkage Triggering the adaptive shrinkage mechanism: r new =r old (1-kΔ)(Contraction coefficient k=0.5) radius from r old =1.2 shrink to r new =1.11, focusing on the distribution of leading companies.

[0166] The variance within clusters decreased by 18%, eliminating noise interference from emerging enterprises.

[0167] 4. Dynamic knowledge graph coupling (in conjunction with step S5b) Injecting event features into weakly related regions of the knowledge graph: 1. Locating weakly related entity pairs: [Target Company] - [Technology Roadmap] -> [Ternary Lithium Battery] (Confidence level 0.59 < 0.6) 2. Event binding feature V E2 With entity attributes: Coupling weight W c =α·τ+(1-α)·μ (α=0.7 is the technology event factor, μ=0.6 is the target company's R&D investment ratio) 3. When W c A new path emerges at >0.65: Solid-state battery breakthrough → cathode material technology replacement → decline in the patent value of ternary lithium batteries. 5. Timeliness calibration of plate characteristics (corresponding to step S33) Establish a dual attenuation model: -Inherent decay: Stale feature weights are calculated according to C t =C0×exp(-λ·Δt) is used for calculation (λ=0.015 / day, Δt=60 days → Ct=0.41). - Event attenuation: When policy event E1 affects the target company, the raw material cost characteristic is additionally attenuated by 30%. It should be noted that after normalization, the weight of high-energy-density technology features accounts for 63%, accurately reflecting the industry's technology shift.

[0168] Those skilled in the art should understand that some technical terms can be understood in the following ways: Sliding time window: This refers to a mechanism in streaming data processing that dynamically extracts consecutive time periods. The window size is set according to business needs (e.g., four quarters). Each time the window slides, the oldest time period data is removed and the latest data is added, ensuring the timeliness and completeness of the analysis dataset. Its core value lies in balancing the inheritance of historical data with the need for real-time response.

[0169] Feature centroid drift: An indicator that quantifies the degree of change in the distribution of plate features using Euclidean distance; the calculation formula is Δ = ||C||. new -C old ‖ / σ, where C is the coordinate vector of the feature center point and σ is the feature standard deviation. This index breaks through the traditional static threshold, realizes a standardized measure of the degree of change, and makes the adjustment of the cluster radius have a mathematical basis.

[0170] Coupling weight: A quantitative value that measures the strength of the association between event features and the inherent attributes of an entity, expressed by formula W. c =α·E+(1-α)·A (where E is the event impact coefficient, A is the entity attribute value, and α is the adjustment factor). This integrates real-time events and steady-state attributes, resolving the correlation distortion problem caused by a single data dimension.

[0171] The above five steps work together to achieve triple optimization: 1. Significant leap in feature library real-time performance: Streaming event capture and incremental clustering mechanisms reduce feature update latency from 14 days to 4 hours. When a lithium ore tariff event occurs, the system recalculates the center point within a quarterly window, improving the response speed of policy-related features by 80% compared to traditional batch processing.

[0172] 2. Significant improvement in noise resistance: Drift-driven adaptive radius shrinkage reduces the variance within clusters to 1.12 (from 1.8), improving the clarity of feature representation for leading companies by 40%. For example, in the solid-state battery incident, patent noise from emerging companies was effectively filtered, increasing the accuracy of technology route identification to 89%.

[0173] 3. Making Implicit Relationships Explicit: The coupling and reconstruction of event features and knowledge graphs increased the coverage rate of technology substitution paths in the industrial chain from 71% to 93%. Target companies thereby discovered the risk of diminishing patent value in ternary lithium batteries, shifted their R&D resources to solid-state batteries, and shortened the patent layout adjustment cycle by 6 months.

[0174] Preferably, this solution integrates incremental clustering with dynamic knowledge graphs, addressing the technical shortcomings of traditional feature libraries lagging behind industry dynamics through three mechanisms: drift quantization, radius adaptation, and event coupling. Test data shows that the accuracy of enterprise technology roadmap prediction has increased by 22%, and the error rate of R&D resource allocation has decreased by 18%.

[0175]

Example 13

[0176] Application scenario setting The target company, CN_BATT_001 (a ternary lithium battery manufacturer), plans to apply for listing on the Science and Technology Innovation Board. An assessment is needed regarding its impact from the triple risks of fluctuating upstream lithium prices, reduced orders from downstream automakers, and the substitution of solid-state battery technology. Data sources include the following items: Business registration data: Three-year financial audit reports of the enterprise (including missing fields) Supply chain data: Purchase contract with CN_LITH_008 (lithium ore supplier) Public opinion data: The incident of CN_AUTO_015 (the car manufacturer) reducing orders by 40% as disclosed on social media. Industry Data: Solid-State Battery Patent White Paper and Technology Roadmap Detailed Step-by-Step Explanation (a) Multi-source data cleaning and standardization (step S11) Optionally, semantic alignment is first performed on the heterogeneous data: 1. Business registration data completion: For the missing "R&D investment ratio" field in CN_BATT_001 for Q2 2023, interpolation was calculated using the moving average method. Q2 value = (Q1 value + Q3 value) / 2, which is 12.3% (the actual value disclosed in the annual report is 12.1%, with an error of 1.6%). 2. Supply chain semantic disambiguation: Mapping "high-nickel cathode material" in the contract to the International Patent Classification H01M4 / 525 to eliminate ambiguity in product names; 3. Quantification of public opinion sentiment: Using the LSTM sentiment analysis model, the text "Battery orders of a certain car company have dropped sharply" is converted into an event intensity coefficient β=0.78 (β∈[0,1], β>0.6 triggers a high-risk marker).

[0177] Those skilled in the art will understand that this step reduces the cross-source entity association error rate from the industry average of 35% to 7.2%, laying the foundation for feature extraction.

[0178] (II) Incremental clustering and construction of the plate feature library (step S12) Perform dynamic feature engineering based on the cleaned dataset: 1. Enterprise Individual Feature Extraction: Calculate the Supply Chain Resilience Index (R) for CN_BATT_001: R = (Inventory turnover rate × 0.4) + (Percentage of spare suppliers × 0.6), and the measured R = 0.67 (the average of leading companies in the industry is 0.58). 2. Clustering based on common characteristics of the new energy battery sector: Perform FCM clustering on the data of the top 10 companies in the new energy battery sector to generate feature centroids: C old =[Average gross profit margin 0.31, annual patent growth rate 15%, public opinion response speed 3.8h]; 3. Incremental Update Trigger: When a breakthrough event in solid-state battery technology is detected, the plate feature center point is recalculated, and the drift amount Δ=‖C is measured. new -C old ||=0.17>threshold θ=0.15, triggering dual optimization: - Decision tree weight redistribution: The weight for technology substitution risk is increased from w_i=0.4 to 0.65; - Adaptive shrinkage of cluster radius: The radius r is reduced from 1.2 to 0.9, focusing on the distribution of leading companies.

[0179] It should be noted that this mechanism reduces the delay in the response technology change of plate characteristics from 14 days to 4.5 hours.

[0180] (III) Growth prediction driven by time decay (step S4 coupled with step S33) When evaluating individual companies based on common characteristics of the sector, a dynamic decay factor is introduced: 1. De-weighting of outdated relationships: CN_LITH_008 has not been updated for 85 days, so its supply relationship confidence level decays exponentially. C t =C0·e^(-λΔt) (λ=0.013 / day, C0=0.85→C t =0.33); 2. Cross-path reliability verification: When the correlation path between the individual feature "dependency on ternary lithium technology" and the sector feature "trend of solid-state batteries" contains a relationship with a confidence level <0.6, the weight of that path is automatically reduced by 35%; 3. Growth Trend Output: Calculate the gross profit margin forecast G for the next three quarters using a comprehensive dynamic weighting method. pred =28.7%, which is closer to the actual value of 29.6% than the 31.5% of the undecayed model (the error is narrowed to 3.0%).

[0181] Preferably, this synergy reduces the time-to-market forecast deviation from ±8.3 months to ±2.9 months.

[0182] In the implementation of the technical solution comprised of the above steps, the implicit technology substitution path is made explicit. Specifically, through incremental clustering and event feature injection, the secondary transmission chain missed by traditional methods is reconstructed: "Solid-state battery patent growth → ternary lithium technology patent devaluation → CN_BATT_001 R&D cost rate increase → gross profit margin decline." This path increases the technology substitution risk coverage from 58% to 86%, and the patent devaluation warning is issued 9 weeks earlier than industry research reports.

[0183] Furthermore, the implementation of the above technical solutions achieves: the attenuation mechanism suppresses static distortion, and chain optimization of drift detection. For example, the confidence attenuation model reduces the weight of outdated supply relationships from 85 days ago to 0.33, while the initial weight W0 of the real-time contracted backup supplier CN_LITH_012 is 0.82. After normalization, the new path dominates (weight ratio 71%), eliminating 6.8 percentage points of evaluation bias caused by data lag.

[0184] When the feature center point drift Δ > θ, the weight redistribution and cluster radius shrinkage produce a synergistic effect: - The increased weighting led to a 62% increase in sensitivity to the risks of technology substitution; - Radius contraction reduces the variance within the feature cluster by 38% (from σ²=1.8 to 1.12); The combined effect of these two factors reduced the deviation recognition speed of the CN_BATT_001 technology route from 6 days to real-time, a 3.2-fold speedup compared to traditional single-dimensional optimization.

[0185] In summary, through semantic alignment in step S11, the enterprise-supplier association error rate decreased by 79%, providing high-fidelity input for step S12; the incremental clustering and drift response mechanism in step S12 shortened the update delay of the segment feature library by 68%; after coupling the time-effect decay in step S33, the gross profit margin prediction error decreased to 3.0%, and the estimated time to market narrowed by 65%. Particularly significant is that the reconstructed solid-state battery technology alternative path breaks through the explicit association limitations of traditional evaluation models, increasing the implicit risk coverage by 48%. This synergistic effect represents substantial progress in the iterative evaluation of power battery technology.

[0186] It should be noted that some of the above terms, such as "confidence decay model," "feature center point drift," and "supply chain resilience index," can be understood as follows: Supply Chain Resilience Index (R) A comprehensive indicator quantifying a company's ability to withstand supply disruptions of antigen materials is derived by linearly weighting inventory turnover rate (weight 0.4) and backup supplier coverage rate (weight 0.6). R > 0.6 indicates high resilience, while R < 0.4 triggers a red alert.

[0187] Feature centroid drift (Δ) The Euclidean distance metric for the distribution variation of common plate characteristics is calculated using the formula Δ = ||C||. new -C old When Δ > dynamic threshold θ (default θ = 0.15), it indicates a significant shift in the industry technology landscape that requires recalibration.

[0188] Coupling weight The quantitative value that measures the strength of the association between event characteristics and the inherent attributes of an entity is given by the formula W. c =α·E+(1-α)·A (where E is the event impact coefficient, A is the entity attribute value, and α is the adjustment factor). This integrates real-time events and steady-state attributes, resolving the correlation distortion problem caused by a single data dimension.

[0189] Confidence Decay Model An exponential function that dynamically reduces the weight of outdated relationships based on data timeliness: C t =C0·e^(-λΔt). Where λ is the decay coefficient (default 0.013 / day), Δt is the number of days the data has not been updated, and C... t ∈[0,1] represents the timeliness and reliability.

[0190]

Example 14

[0191] Step 2: Dynamic Knowledge Graph Construction Building an event-driven knowledge graph based on fused data: 1. Entity relationship initialization: Establish a core relationship chain [CN_BATT_001] - [Procurement] -> [CN_LITH_008] (strength 0.9) 2. Real-time event response: Event E1 captured: "Lithium ore exporting countries impose 20% tariff" (Event type = policy, impact coefficient γ = 0.85) Automatically generate a new relation: [CN_LITH_008]-[Tariff Transmission]->[CN_BATT_001] (Initial confidence level C0=0.75) 3. Topological dynamic evolution: When logistics event E2 is detected: "15-day strike at Durban Port, South Africa", a relationship weight update is triggered. W new =W old ×(1-δ t )(δ t =0.3 is the event impact factor) The strength of the purchasing relationship decreased from 0.9 to 0.63. It should be noted that the event response delay is less than 1 minute, resulting in a 15-fold improvement in efficiency compared to traditional batch processing. It should be understood that semantic alignment here refers to the process of mapping entity identifiers from different sources to a unified standard system. This application adopts a three-level mapping mechanism: 1) mandatory matching of business registration codes; 2) verification through contract filing numbers and equity tree; 3) natural language entity disambiguation. Thus, by integrating rule matching and deep learning, the problem of broken associations caused by "same name, different companies" and "same company, multiple names" is solved.

[0192] Event-driven knowledge graph: a dynamic network architecture that triggers topology evolution through real-time business events. Its core features include: 1) an event parsing engine transforms unstructured information (such as policy text) into structured feature vectors; 2) event features are automatically bound to entity attributes to generate new relationships; and 3) relationship weights dynamically decay with the event's timeliness. This design overcomes the predefined limitations of static graphs, enabling real-time capture of the risk transmission path in the industrial chain.

[0193] Technical problems of data fusion to resolve cross-source ambiguity Traditional solutions rely on fuzzy name matching, resulting in a 41% incorrect association rate between "Shenzhen Lithium Energy Technology" (a subsidiary) and "Shenzhen Lithium Energy Group" (the parent company). This solution addresses this through equity penetration and BERT disambiguation: -Subsidiary contract HT2023_AC108 precisely correlates with parent company CN_LITH_008 - The public opinion poll regarding the "suspension of South African lithium mine project" is automatically linked to subsidiary CN_LITH_008-02. The accuracy of entity association has been improved to 94.5%, laying a data foundation for risk transmission analysis.

[0194] Dynamic graphs capture hidden supply chain risks In the lithium ore tariff incident: 1. Within one minute of policy release, a transmission path from [Tariff Policy] to [Raw Material Costs] will be generated. 2. Calculate the potential for cost increases by considering the inventory terms in the purchase contract: ΔC=(P t ×Q×γ) / Inventory_t(P t (where Q is the unit price and Q is the quantity purchased) Battery costs are projected to increase by 18.7% (difference from actual financial statements is less than 2%). 3. When a port strike occurs, the logistics reliability weight is automatically lowered, triggering a procurement strategy warning. Two-step collaboration improves the real-time performance of decision-making Data fusion provides accurate entity relationships (step 1), enabling knowledge graphs to focus on effective risk transmission chains (step 2). When export controls and strikes occur concurrently: Step 1: Disambiguation confirms that "CN_LITH_008-02" is the affected entity. Step 2 generates a multi-level transmission path: Policy -> Subsidiary Supply -> Target Enterprise Capacity Risk warning speed has been reduced from the industry average of 6 hours to 9 minutes, and the procurement strategy adjustment cycle has been shortened by 60%.

[0195] This application addresses the unique challenge of managing multi-level supply relationships in the new energy vehicle battery industry through deep coupling of semantic alignment and dynamic graphs. Tests show that the accuracy rate of predicting production stoppage risks due to supply chain disruptions for target companies reaches 92%, and the optimization rate of raw material reserves is improved by 33%, providing a highly reliable data fusion paradigm for technology-intensive industries.

[0196] Figure 3 This is a block diagram illustrating an apparatus 800 for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data, according to an exemplary embodiment. For example, apparatus 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0197] Reference Figure 3 The device 800 includes: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0198] The processing component 802 may include one or more processors 820 to execute the instructions corresponding to the steps of the method. Furthermore, the processing component 802 may include one or more multimedia modules for interacting with other components.

[0199] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of this data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0200] The power supply component 806 provides power to the device 800 and includes a power management system and a power supply. The multimedia component 808 includes a screen that provides an output interface and includes a liquid crystal display and a touch panel; when a touch panel is included, the screen is implemented as a touchscreen to receive user input signals. The multimedia component 808 may also include a front-facing camera and / or a rear-facing camera to acquire external multimedia data.

[0201] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0202] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0203] Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of device 800. For example, sensor assembly 814 may detect the on / off state of device 800, the relative positioning of components such as the display and keypad of device 800, changes in the position of device 800 or a component of device 800, the presence or absence of user contact with device 800, the orientation or acceleration / deceleration of device 800, and temperature changes of device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0204] Communication component 816 is configured to facilitate wired or wireless communication between device 800 and other devices. Device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0205] In an exemplary embodiment, the apparatus 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0206] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of the device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0207] In an exemplary embodiment, a non-transitory computer-readable storage medium is also provided, which, when the instructions in the storage medium are executed by the processor of a mobile terminal, enables the mobile terminal to execute any of the enterprise growth and IPO potential assessment and prediction methods based on multi-source heterogeneous data in other embodiments.

Claims

1. Methods for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data, including: S11 collects multi-source heterogeneous data and performs cleaning processing to generate standardized enterprise analysis datasets; S12 Based on the enterprise analysis dataset, extract multi-dimensional enterprise features and construct a comprehensive profile library through cluster analysis, including individual enterprise features and common features of sectors; S4. Using the common characteristics of the sectors in the comprehensive profile database as a reference, evaluate the individual characteristics of the enterprise and predict the growth trend of the individual enterprise.

2. The method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data as described in claim 1, wherein, Step S12 further includes: The multi-source heterogeneous data is fused to generate fused data; Based on the fused data, a dynamic enterprise knowledge graph is constructed for the comprehensive profile database; Perform incremental clustering analysis to process the newly added enterprise data and recalculate the feature centroids of the sectors, generating a dynamically updated database of common features of the sectors; If the drift of the feature center point of the plate exceeds a preset threshold, the feature weight reassignment operation of the decision tree model is triggered to optimize the plate common feature evaluation system. By combining the entity relationship confidence decay mechanism of the dynamic enterprise knowledge graph, the timeliness status of outdated segment features is mapped to the evaluation weight decay factor, and the common features of segments after timeliness verification are output to the comprehensive profile library.

3. The method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data according to claim 2, wherein, Step S4 further includes: Based on the common features of the sectors in the comprehensive profile database, a multi-dimensional weighted similarity calculation model is constructed to calculate the weighted feature distance between the individual features of the enterprise and the common features of the sectors to generate an initial similarity value; Combining the entity relationship confidence decay mechanism of the dynamic enterprise knowledge graph, the matching path between the individual characteristics of the enterprise and the common characteristics of the sector is verified to see if it crosses entity relationships with confidence levels below a preset threshold, and a path reliability score is generated. The initial similarity value is corrected based on the path reliability score, and the enterprise individual characteristic evaluation result with timeliness tag is output; Based on the assessment results of the individual enterprise characteristics, the growth trend of the individual enterprise is predicted.

4. The method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data as described in claim 3, wherein, Step S4 further includes: A multivariate prediction model is trained based on historical data of the individual characteristics of the enterprise, and an initial prediction value is generated by dynamically adjusting the weight coefficients of revenue growth rate and net profit growth rate through a parameter optimization algorithm. Combining the entity relationship confidence decay mechanism of the dynamic enterprise knowledge graph, real-time incremental event features are injected to correct the initial prediction value, and the predicted distribution of financial indicators with time-sensitive labels is output. The visualization analysis engine maps the predicted distribution of the financial indicators into a dynamic deviation heatmap, marks the fluctuation range of key indicators, and feeds it back to the parameter optimization module of the multivariate prediction model.

5. The method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data according to claim 4, wherein, Step S4 further includes: Based on the predicted growth trend of the individual enterprises, a timeline model for enterprise compliance is constructed and the growth trajectory of key financial indicators is simulated by matching the preset quantitative indicators for listing in the securities market. By combining the entity relationship confidence decay mechanism of the dynamic enterprise knowledge graph, the probability of the key financial indicators meeting the target in the time axis model is calculated and a listing time prediction value with confidence interval is generated. The visualization engine maps the predicted listing time to the corresponding probability of achieving the target as a heat map time axis, marks the key indicator gap data, and outputs it to the decision terminal.

6. The method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data as described in claim 1, wherein, Step S11 further includes: S1 performing fusion processing on the multi-source heterogeneous data to generate fused data; The steps and methods also include: S2. Based on the fused data, a dynamic enterprise knowledge graph is constructed for the comprehensive profile database to enable the real-time evolution of entity relationships between enterprise entities and multi-source heterogeneous enterprise data; and, S3 uses the dynamic enterprise knowledge graph to perform intelligent decision-making simulations and feeds the simulation results back to the dynamic enterprise knowledge graph to optimize its evolution.

7. The method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data as described in claim 1 or 5, wherein, Step S12 further includes: Perform incremental clustering on the newly added enterprise data and recalculate the feature centroids of the sector. Based on the drift of the feature center point, the feature weights of the decision tree are reassigned. The method further includes: using the common features of the modules in the comprehensive profile library as a reference, and combining the entity relationship confidence decay mechanism of the dynamic enterprise knowledge graph, to perform enterprise individual growth trend prediction; The confidence decay mechanism further includes: Map the time-related status of outdated features to an evaluation weight decay factor; When the matching path between individual characteristics and common characteristics of the module crosses entity relationships in the knowledge graph with a confidence level below the threshold, the reliability score of that evaluation path is automatically reduced.

8. The method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data as described in claim 6, wherein, Step S1 further includes: S1a) Perform semantic alignment on the multi-source heterogeneous enterprise data to generate a unified enterprise identifier; S1b) Based on the unified enterprise identifier, associate business, public opinion and supply chain data to form the fused data.

9. The method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data as described in claim 8, wherein, Also includes: S1c) Perform semantic disambiguation on the business registration, public opinion, and supply chain data in the cross-source data of the multi-source heterogeneous enterprises to generate the unified enterprise identifier; S1d) Based on the unified enterprise identifier, the cross-source data associated with the same enterprise entity forms the fused data.

10. The method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data according to claim 6, wherein, Step S3 further includes: S36) Extract the key impact paths of risk transmission or opportunity association in the dynamic enterprise knowledge graph; S37) Generate a visual topology map of the associated paths and label the risk probability or opportunity weight of the inference results; S38) Simulate the risk transmission between multiple entities along the key impact path, and perform multi-dimensional prediction and deduction of the business opportunities of the industrial chain to which the multiple entities belong.

11. The method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data according to claim 6, wherein, Steps S1, S2, and S3 respectively include: S1' integrates multi-source heterogeneous enterprise data and generates associative fused data through semantic alignment; S2' constructs an entity relationship network based on the fused data to achieve event-driven real-time evolution; S3' uses the entity relationship network to perform decision deduction and feeds the deduction results back to the entity relationship network to optimize its evolution.

12. The method for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data according to claim 1, wherein, Step S12 further includes: S121. Generate a preliminary set of companies by integrating the lists of listed companies in the target sector provided by multiple data sources; S122. By cross-comparing the preliminary set of enterprises, determine the list of concept listed companies that meet the preset time period, and filter the list of target companies based on the principle of business relevance; S123. Identify common characteristics of the enterprises in the target enterprise list to generate common characteristics of the sector; S124. Based on the common characteristics of the aforementioned sectors, analyze the multi-dimensional analytical indicators of the target enterprise and integrate the analysis results into the comprehensive profile database.

13. An electronic device for assessing and predicting enterprise growth and IPO potential based on multi-source heterogeneous data, comprising: monitor; One or more processors; Memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods according to claims 1 to 12.

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