Data management cloud platform and project pushing method for industrial and commercial investment and financing

Abstract

The invention discloses a data management cloud platform for industrial and commercial investment and financing and a project pushing method, relates to the technical field of data management, and is used for solving the problem of low accuracy of a project pushing strategy in a complex market environment. The method comprises the following steps: obtaining multi-source heterogeneous data and carrying out standardization processing, generating a consistency data set, extracting key features and carrying out fuzzification processing, and triggering and updating an association model in real time by using a constructed data source association degree matrix, so as to ensure that data association reflects a current market environment; a conformal prediction interval is constructed through non-consistency scores, a push decision matrix is generated in combination with fuzzy reasoning, a decision boundary is dynamically optimized by using Bayesian reasoning, a push effect is monitored in real time, and an operation signal is generated according to changes, so that the data management efficiency is improved, and the accuracy, adaptability and stability of a project push strategy are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a data management cloud platform and a project push method for industrial and commercial investment and financing. Background Art

[0002] The data management cloud platform and project push method for industrial and commercial investment and financing came into being against the backdrop of the rapid development of the digital economy and the increasingly complex investment and financing needs of enterprises. With the continuous changes in the market environment, the amount of information faced by enterprises is huge and complex. Traditional investment and financing decisions often rely on manual judgment, which is inefficient and easily affected by subjective factors. In order to improve decision-making efficiency, cloud computing and big data technologies have been introduced to build a cloud platform integrating data management, analysis and intelligent push.

[0003] The existing technology has the following deficiencies: In the industrial and commercial investment and financing scenarios, investment and financing push strategies often rely on preset decision boundaries, which are usually based on historical data or fixed rules. However, the market environment changes rapidly, and factors such as economic indicators, industry trends and investor behavior will have an immediate impact on market dynamics, but existing decisions are usually unable to capture and respond to these changes in real time. Due to the lack of dynamic analysis and optimization mechanisms for real-time market data, decision boundaries cannot be adjusted in time, resulting in a delay in the push strategy when facing sudden market changes. Therefore, the investment and financing push strategy still uses boundary conditions that are no longer applicable and cannot adapt to the new market situation, thereby increasing investment risks and missing potential market opportunities, seriously affecting the competitiveness and decision-making accuracy of enterprises in complex market environments.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a data management cloud platform and a project push method for industrial and commercial investment and financing, so as to solve the problem of poor accuracy of project push strategies in a complex market environment proposed in the background technology.

[0006] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a data management cloud platform and project push method for industrial and commercial investment and financing, comprising the following steps:

[0007] Acquire multi-source heterogeneous data in industrial and commercial investment and financing scenarios, perform standardization and format unification to generate consistent data sets, and perform feature extraction and fuzzification on the standardized data sets;

[0008] Construct a correlation matrix between data sources, trigger the update mechanism of the correlation model according to the correlation score, and adjust the correlation matrix through an adaptive update algorithm;

[0009] Conformal prediction intervals are constructed based on the inconsistency scores of data points, and integrated analysis of multi-source data is performed through fuzzy reasoning functions. The push decision matrix is ​​generated by combining fuzzy features and correlation matrix.

[0010] Use Bayesian reasoning to optimize the decision boundary of the push strategy, and dynamically adjust the push strategy by combining prior information with current market observation data;

[0011] Conduct real-time monitoring and analysis on the push results after decision boundary optimization, obtain and analyze the push change information generated during the project push process, generate different signals based on the analysis results, and adjust the cloud platform push strategy.

[0012] Preferably, multi-source heterogeneous data in industrial and commercial investment and financing scenarios are obtained, standardized and formatted to generate a consistent data set, and feature extraction and fuzzification processing are performed on the standardized data set, including:

[0013] Standardize multi-source heterogeneous data, including timestamp alignment, unit conversion, data type conversion, and format unification;

[0014] Perform feature extraction and fuzzification processing on the standardized data;

[0015] Use principal component analysis or independent component analysis to extract key features, including cash flow, debt-to-asset ratio, and market share;

[0016] The extracted features are converted into fuzzy sets by applying fuzzy membership functions.

[0017] Preferably, a correlation matrix between data sources is constructed, an update mechanism of the correlation model is triggered according to the correlation score, and the correlation matrix is ​​adjusted by an adaptive update algorithm. The specific steps include:

[0018] Map multi-source heterogeneous data into a high-dimensional feature space and perform fuzzy processing on each feature dimension;

[0019] Calculate the membership differences between different data points on each fuzzy set as the association score;

[0020] Construct a correlation matrix between data sources and compare it with the correlation threshold to determine the correlation model and automatic update trigger;

[0021] When the correlation score between two data sources is lower than the correlation threshold, the update mechanism of the correlation model is automatically triggered;

[0022] After the association update mechanism is triggered, the cloud platform uses an adaptive update algorithm to reconstruct the association model between data sources by adjusting the inconsistency score and fuzzy set membership.

[0023] Preferably, a conformal prediction interval is constructed according to the inconsistency score of the data point, and an integrated analysis of multi-source data is performed through a fuzzy inference function. The push decision matrix is ​​generated by combining the fuzzy features and the correlation matrix. The specific steps are as follows:

[0024] Conformal prediction and fuzzy integrated analysis of multi-source heterogeneous data;

[0025] Calculate the inconsistency score for each data point, and compare the inconsistency scores of the data points to determine the upper and lower limits of the conformal prediction interval for each data point;

[0026] Determine the lower and upper bounds of the conformal prediction interval based on the confidence level;

[0027] A fuzzy rule base is constructed according to different data sources and conformal prediction intervals, and the fuzzy membership of each conformal prediction interval is calculated according to the constructed fuzzy rule base;

[0028] Bringing the fuzzy membership into the fuzzy reasoning function generates a decision output as the fuzzy reasoning result;

[0029] All fuzzy reasoning results are merged into a push strategy, and all fuzzy reasoning outputs are combined to form a push decision matrix through fuzzy logic and optimization algorithms as the actual push strategy for investment and financing projects.

[0030] Preferably, Bayesian reasoning is used to optimize the decision boundary of the push strategy, and the push strategy is dynamically adjusted by combining prior information with current market observation data, including:

[0031] The cloud platform integrates prior information from historical data and expert knowledge, including the company's historical performance, market volatility, and industry trends, and quantifies the prior information into prior probability distribution;

[0032] Introducing observational data, which includes current market conditions, real-time financial data of enterprises, and feedback from investors;

[0033] Calculate the likelihood function of the observed data and modify the prior probability distribution based on the likelihood function;

[0034] By combining the prior probability distribution with the likelihood function through the Bayesian formula, the posterior probability distribution is calculated;

[0035] According to the posterior probability distribution, the cloud platform dynamically adjusts the decision boundary of the push strategy.

[0036] Preferably, the push results after the decision boundary is optimized are monitored and analyzed in real time, and the push change information generated during the project push process is obtained and analyzed. The specific steps are as follows:

[0037] Obtain push change information generated during project push, including associated response information and market adaptation information;

[0038] The correlation response information includes the correlation delay adjustment index, and the market adaptation information includes the market signal sensitivity index:

[0039] The correlation delay adjustment index and the market signal sensitivity index are combined to generate the push stability coefficient;

[0040] The correlation delay adjustment index is inversely proportional to the push stability coefficient, and the market signal sensitivity index is directly proportional to the push stability coefficient.

[0041] Preferably, different signals are generated according to the analysis results to adjust the cloud platform push strategy, and the specific steps include:

[0042] Compare the generated push stability coefficient with the push control threshold;

[0043] If the push stability coefficient is greater than the push control threshold, a project push strategy stability signal is generated, and no additional push strategy adjustment is required;

[0044] If the push stability coefficient is less than or equal to the push control threshold, a project push strategy control signal is generated, the cloud platform re-evaluates the correlation between the market signal and the data source, and updates the push strategy.

[0045] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0046] The present invention provides an efficient method for optimizing the push strategy of industrial and commercial investment and financing. The method obtains and processes multi-source heterogeneous data, performs standardization processing and unified format to generate a consistent data set, thereby ensuring the consistency and comparability of the data. The standardized data set is further subjected to feature extraction and fuzzification processing to extract key features reflecting market dynamics. The method constructs a correlation matrix between data sources, triggers an update mechanism of a correlation model according to the correlation score, and uses an adaptive update algorithm to adjust the correlation matrix in real time, thereby ensuring that the correlation between data sources always reflects the current market environment and data characteristics, thereby improving the accuracy and adaptability of push decisions.

[0047] In addition, by calculating the inconsistency scores of data points, constructing conformal prediction intervals, and combining fuzzy reasoning functions to integrate and analyze multi-source data, the push decision matrix is ​​effectively generated, and the decision boundary of the push strategy is optimized using Bayesian reasoning. The strategy is dynamically adjusted to adapt to market changes, and the push effect is optimized through real-time monitoring. Finally, in the process of project push, different operation signals are generated through real-time monitoring and analysis of push change information to ensure that the cloud platform can adjust the push strategy in time to cope with dynamic changes in the market, thereby greatly improving the stability, accuracy and adaptability of the cloud platform's project push strategy in a complex market environment, effectively reducing the project investment and financing risks, and improving the project investment return rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0049] Figure 1 The present invention is a flow chart of a data management cloud platform and a project push method for industrial and commercial investment and financing. DETAILED DESCRIPTION

[0050] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0051] Embodiment 1: The present invention provides Figure 1 The data management cloud platform and project push method for industrial and commercial investment and financing shown in the figure include the following steps:

[0052] Acquire multi-source heterogeneous data in industrial and commercial investment and financing scenarios, perform standardization and format unification to generate consistent data sets, and perform feature extraction and fuzzification on the standardized data sets;

[0053] Construct a correlation matrix between data sources, trigger the update mechanism of the correlation model according to the correlation score, and adjust the correlation matrix through an adaptive update algorithm;

[0054] Conformal prediction intervals are constructed based on the inconsistency scores of data points, and integrated analysis of multi-source data is performed through fuzzy reasoning functions. The push decision matrix is ​​generated by combining fuzzy features and correlation matrix.

[0055] Use Bayesian reasoning to optimize the decision boundary of the push strategy, and dynamically adjust the push strategy by combining prior information with current market observation data;

[0056] Conduct real-time monitoring and analysis on the push results after decision boundary optimization, obtain and analyze the push change information generated during the project push process, generate different signals based on the analysis results, and adjust the cloud platform push strategy.

[0057] In the data management cloud platform and project push scenarios for industrial and commercial investment and financing, the platform needs to process heterogeneous data from multiple sources. The data may include corporate financial statements, market trend analysis, industry research reports, investor behavior data, and macroeconomic indicators.

[0058] The data sources are diverse, that is, there are differences in data format, precision, timestamp, and data integrity. The data is uniformly preprocessed and feature engineered to convert the heterogeneous data into a consistent format suitable for analysis, and to extract key features that reflect multi-level associations. The specific steps are as follows:

[0059] Standardize and unify the format of data from different sources to ensure data consistency and usability, including:

[0060] Perform timestamp alignment to align the timestamps in all data sources to a standard time scale (such as day, month, quarter) to ensure that time series data can be correctly parsed and compared in the cloud platform;

[0061] Perform unit conversion and unify the units of financial data and market data from different sources (e.g., unify to U.S. dollars or RMB) to eliminate unit differences between different data sources and ensure data comparability;

[0062] Perform data type conversion to convert non-numeric data (such as dates, Boolean values, and categorical variables) in the data source into numeric data or encoded categorical variables suitable for processing by the cloud platform;

[0063] Standardization and format unification are carried out to convert different formats and units of all data sources into a consistent standard form, ensuring that features such as time series and financing-related data can be uniformly stored and processed in the cloud platform, eliminating the heterogeneity between different data sources. The standardization formula is as follows: ,in, is the value of the i-th data point on the j-th feature after standardization, is the original value, is the central value (mean) of the jth feature, is the scale parameter of the feature (e.g. used for magnitude adjustment)

[0064] After standardization and format unification, the standardized data obtained It will be used as input for feature engineering to extract key features.

[0065] Perform feature extraction and fuzzification processing to extract key features reflecting multi-level associations from the cleaned data and perform fuzzification processing on these features, including:

[0066] Use dimensionality reduction techniques such as principal component analysis (PCA) or independent component analysis (ICA) to extract the most representative key features (such as cash flow, debt-to-asset ratio, market share, etc.);

[0067] Apply fuzzy membership function to the extracted features to convert precise values ​​into fuzzy sets and enhance the ability to handle data uncertainty. The formula is: ,in, represents the fuzzy representation of the i-th data point on the extracted key features, for The degree of membership to the fuzzy set P, is its membership degree to the fuzzy set Q;

[0068] In the feature extraction stage, key features that can explain most of the data variability are extracted from the preprocessed data through PCA or ICA. The key features can reflect the key indicators in industrial and commercial investment and financing, such as corporate financial health and market competitiveness. These key features are fuzzified through fuzzy membership functions to generate membership representation of fuzzy sets, thereby enhancing the cloud platform's ability to handle data uncertainty.

[0069] Based on the generated fuzzy set, the cloud platform dynamically adjusts the correlation and optimizes the push strategy of investment and financing projects. The specific steps are as follows:

[0070] Multi-source heterogeneous data are mapped to a high-dimensional feature space, and each feature dimension is fuzzified. Through the fuzzy membership function, the precise numerical features are converted into fuzzy sets to capture the uncertainty in the data. The formula is described as: ,in, and are the kth fuzzy sets Medium data point and The membership degree of , ϵ is a small constant to avoid division by zero, and K is the number of fuzzy sets;

[0071] The cloud platform calculates the differences in the membership of different data points on each fuzzy set to obtain the corresponding correlation scores. A higher correlation score indicates a strong correlation between data sources, while a lower score may indicate that the correlation between data sources is weakening or failing. The cloud platform monitors the correlation scores in real time to ensure that the relationship between data sources remains stable and can quickly respond to any potential risk of correlation failure.

[0072] A correlation matrix between data sources can be constructed, and the data source correlation scores in the correlation matrix can be calculated in real time to determine whether the correlation model needs to be updated and optimized.

[0073] Perform correlation threshold judgment and automatic update triggering. That is, the cloud platform sets a correlation threshold. When the correlation score between any two data sources is lower than the threshold, the update mechanism of the correlation model is automatically triggered. The update mechanism is based on the intelligent algorithm of the cloud platform, dynamically adjusts the model parameters, and re-optimizes the correlation between data sources. When there is a problem with the correlation between data sources, the cloud platform can respond quickly and adjust the model parameters to restore and optimize the data correlation, thereby maintaining the stability of the system and the accuracy of push decisions;

[0074] After the correlation update mechanism is triggered, the correlation model is dynamically updated and optimized, that is, the cloud platform will dynamically adjust the parameters of the correlation model to optimize the correlation between data sources. Specifically, the cloud platform uses an adaptive update algorithm to reconstruct the correlation model between data sources by adjusting the inconsistency score and fuzzy set membership to reflect the data relationship in the current market environment: , ,in, is the updated correlation matrix, λ is the update coefficient, is the correlation matrix;

[0075] The cloud platform dynamically updates the values ​​of the correlation matrix through an adaptive algorithm to ensure that the updated correlation model is more consistent with the actual correlation of the current data. The update process takes into account the real-time correlation between each data source. By optimizing the inconsistency score, the cloud platform can adjust and strengthen the correlation between data sources, so that the model can adapt to the changing market environment and provide optimized data input for subsequent conformal prediction and fuzzy integration. The updated correlation matrix will be used for the conformal prediction interval construction and fuzzy integration analysis in the subsequent steps to ensure that the investment and financing projects can be pushed and decided based on the latest correlation model.

[0076] After completing the unified preprocessing and feature engineering of data sources and the dynamic correlation monitoring and updating mechanism, the cloud platform further performs conformal prediction and fuzzy integration analysis on multi-source heterogeneous data to improve the accuracy and reliability of investment and financing project push. The specific steps are as follows:

[0077] The cloud platform needs to calculate the nonconformity score for each data point. The nonconformity score is used to measure the difference between a data point and its similar data points, that is, the degree of abnormality of the data point in the current data set. The higher the score, the greater the difference between the data point and other data points, and the more likely it is an outlier. The calculation of the nonconformity score is based on the extracted fuzzy feature representation and the optimized correlation matrix. The nonconformity score of each data point is calculated through the membership of the data point in the fuzzy set and its correlation with other data points.

[0078] The cloud platform combines fuzzy feature representation with the correlation matrix to analyze data points one by one, calculate the difference between them and other data points on the fuzzy set, and compare the differences between data points. The cloud platform identifies data points that are more consistent with the current data pattern and data points with large deviations.

[0079] Determine the upper and lower limits of the conformal prediction interval. After the inconsistency score calculation is completed, the cloud platform determines the upper and lower limits of the conformal prediction interval for each data point based on the inconsistency score of the data point;

[0080] It should be noted that the conformal prediction interval is a range that covers possible real values ​​in the future. The width of this range is determined by the confidence level set by the cloud platform. The higher the confidence level, the wider the interval range and the greater the possibility of coverage. The cloud platform will use the inconsistency score to sort the predicted value and score of each data point, and extract the upper and lower limits corresponding to the confidence level as the final conformal prediction interval;

[0081] The cloud platform selects an appropriate score range from the sorted inconsistency scores based on the set confidence level (for example, 95% or 99%), and determines the lower and upper limits of the conformal prediction interval, so that the actual value falls within the conformal prediction interval with a high probability.

[0082] After initially determining the conformal prediction interval, the cloud platform makes necessary adjustments and optimizations to the conformal prediction interval. Due to the diversity and relevance of data, the conformal prediction interval may need to be fine-tuned according to the actual application scenario. For example, for predictions of specific industries or projects, it may be necessary to consider industry characteristics or market fluctuations and scale or smooth the conformal prediction interval. In addition, the cloud platform can also adjust the conformal prediction interval in real time based on the latest results of dynamic correlation monitoring to ensure that it always reflects the latest data relationships.

[0083] The cloud platform adjusts the conformal forecast interval based on the latest correlation monitoring information. These adjustments may include narrowing or widening the interval range, or smoothing the forecast value within the interval to more accurately reflect the market dynamics and real changes in data;

[0084] After adjustment and optimization, the final conformal prediction interval will be generated in the cloud platform and used as the input for subsequent fuzzy integration analysis.

[0085] After completing the construction of the conformal prediction interval, the cloud platform needs to integrate the prediction results from different data sources to form a unified push decision. Through the calculation of fuzzy rules and membership, the conformal prediction intervals of multi-source data are integrated into a comprehensive decision output. When dealing with the uncertainty and ambiguity of multi-source data, an optimized and unified decision-making framework is provided, thus laying the foundation for the accurate push of investment and financing projects. The specific processing steps are as follows:

[0086] The cloud platform builds a fuzzy rule base based on the characteristics of different data sources and conformal prediction intervals. The fuzzy rule base consists of a series of "if-then" rules. Each rule defines the logical relationship between different fuzzy sets, and formulates fuzzy rules applicable to the current investment and financing scenarios by analyzing the fuzzy feature representation and correlation matrix generated above. For example, in the investment and financing scenarios of the cloud platform, there are two types of fuzzy sets: corporate financial status and market trends; the fuzzy rules can be: if the financial status of an enterprise belongs to the "stable" set, and the market trend belongs to the "rising" set, then the push priority of the enterprise project should be increased;

[0087] The fuzzy rule base combines fuzzy features and correlation matrix through the "if-then" rule structure, and defines how to process the prediction results of data sources under different conditions. The construction of the rule base depends on the combination of fuzzy sets and membership. Through the rules, the cloud platform can establish logical associations between different data sources. The constructed fuzzy rule base will be used in the subsequent fuzzy reasoning process to guide the fusion of different conformal prediction intervals.

[0088] The cloud platform calculates the fuzzy membership of each conformal prediction interval based on the constructed fuzzy rule base. The fuzzy membership reflects the degree of membership of a data point in a specific fuzzy set and is used to weigh the influence of different conformal prediction intervals. The fuzzy membership calculation is based on the definition of the fuzzy set and is combined with the correlation matrix for correction to ensure that the fuzzy membership can accurately reflect the status of the current data source. The membership calculation formula is: ,in, For data points In the fuzzy set The membership degree in is the fuzzy feature representation, is the correction term of the correlation matrix, N is the total number of data points;

[0089] The calculation of fuzzy membership is based on the position of the data point in the fuzzy set, combined with the correction of the correlation matrix, to ensure that the membership accurately reflects the current status of each data source. The higher the membership, the closer the position of the data point in the fuzzy set is to the center, and the greater the influence.

[0090] After calculating the fuzzy membership of the conformal prediction interval, the cloud platform brings these fuzzy memberships into the fuzzy reasoning function for comprehensive analysis. The fuzzy reasoning function combines each membership and fuzzy rules to generate a comprehensive decision output. The expression of the fuzzy reasoning function output is: , and are the fuzzy membership degrees, is the weight distribution of the fuzzy inference function;

[0091] After obtaining the output of the fuzzy reasoning function, the cloud platform synthesizes all the fuzzy reasoning results to generate a unified push strategy, and combines all the fuzzy reasoning outputs to form an optimal push decision matrix through fuzzy logic and optimization algorithms to guide the actual push strategy of investment and financing projects. The expression is: , ,in, is the final decision matrix, is the weight of the mth fuzzy inference result, is the output of the fuzzy inference function, M is the total number of fuzzy rules;

[0092] The weight of each inference result Determined according to the relative size of its output values, it ensures that more influential reasoning results occupy a larger proportion in the final decision. The synthesized decision matrix provides a unified push strategy for the cloud platform, which can effectively guide the push of investment and financing projects and make the best choice when facing complex market conditions.

[0093] After completing fuzzy integrated analysis and decision synthesis, the cloud platform has generated a preliminary push strategy. Due to the uncertainty of the market environment and data, the decision boundary still needs to be further optimized to ensure that the push strategy can dynamically adapt to the actual situation. Through Bayesian reasoning, the cloud platform combines prior information and the results of fuzzy reasoning to optimize the decision boundary of the push strategy, so that the final push result is more in line with actual needs and changes in the market environment. The specific steps are as follows:

[0094] The cloud platform integrates prior information from historical data and expert knowledge, including the company's historical performance, market volatility, industry trends, etc., and is quantified into prior probability distribution. Prior information is used to provide an initial decision-making basis in the Bayesian reasoning process, and is combined with subsequent observation data to correct the push strategy. The prior probability distribution obtained by integration will be used as input and passed to the next step of Bayesian reasoning for posterior probability calculation combined with observation data;

[0095] For example, suppose that in past market data, the probability of a certain type of enterprise being successful under certain market conditions is 70%. This historical data will be used as a priori probability, indicating the possibility of the enterprise being successfully pushed in the absence of other observed data;

[0096] The cloud platform then introduces observation data, which includes the current market status, the company's real-time financial data, investor feedback, etc. By calculating the likelihood function, the likelihood of these observation data under prior conditions is evaluated. The likelihood function reflects the degree of consistency between the observation data and the prior information, and is used to correct the prior probability, thereby calculating the posterior probability distribution;

[0097] For example, assuming that the current market data shows that the success probability of an enterprise under similar conditions is 60%, the cloud platform calculates the likelihood function of this observation data under the prior probability, indicating the degree of support for the successful push of the enterprise by the current market status;

[0098] The cloud platform uses the Bayesian formula to combine the prior probability distribution with the likelihood function to calculate the posterior probability distribution. The posterior probability distribution reflects the optimization result of the push strategy after combining the observed data.

[0099] For example, suppose the cloud platform is making a decision on the investment and financing push strategy for an enterprise project. The cloud platform uses Bayesian reasoning combined with prior information and current market data to dynamically adjust the decision boundary of the push strategy to improve the success rate of the push;

[0100] Prior information is that the cloud platform has historical data on successful financing of enterprises over a period of time. For example, in the past, the success rate of a certain type of enterprise in obtaining financing during a stable economic period was 70%. Based on historical data, the cloud platform believes that the prior probability of the current enterprise successfully obtaining financing under similar conditions is P (success) = 0.7;

[0101] The observation data are: market data show that the success rate of similar enterprises in the current economic environment has dropped to 50%;

[0102] The cloud platform calculates the probability of successful financing of enterprises under the current market conditions and obtains the likelihood function: (observed data | success) = 0.5. According to the Bayesian formula, the posterior probability obtained after calculation is 0.55.

[0103] Dynamically adjust the decision boundary. The original decision boundary assumes that under a priori conditions, the push strategy decision boundary of the cloud platform is set to push when the prediction success rate is higher than 60%;

[0104] The adjusted decision boundary is calculated through the posterior probability. The cloud platform found that the success rate of enterprises in the current market environment has dropped to 55%. In order to adapt to this change, the cloud platform decided to adjust the decision boundary to a more conservative 65%;

[0105] If the prediction success rate of a certain enterprise is calculated as 55% through the posterior probability, it may continue to be pushed under the original decision boundary, but under the adjusted decision boundary, the cloud platform decides not to push it to avoid risks.

[0106] According to the posterior probability distribution, the cloud platform dynamically adjusts the decision boundary of the push strategy. The optimization of the decision boundary ensures that the push strategy can maintain adaptability in a changing market environment and avoid overly conservative or aggressive push decisions. The optimized decision boundary is used in actual push execution to maximize the success rate and return on investment. After optimizing the decision boundary, the cloud platform executes the final push decision and monitors and provides feedback on the push results in real time.

[0107] Conduct real-time monitoring and analysis on the push results after decision boundary optimization, and determine whether the project push strategy needs to be adjusted based on the analysis results. The specific steps are as follows;

[0108] Obtain push change information generated during project push and analyze the push change information, which includes associated response information and market adaptation information;

[0109] The associated response information includes an associated delay adjustment index, and the market adaptation information includes a market signal sensitivity index. After collection, the associated delay adjustment index and the market signal sensitivity index will be associated.

[0110] The Delayed Association Adjustment Index (DAAI) is an indicator used to measure the time difference between the change in the degree of association between data sources and the adjustment of the cloud platform decision model. It reflects whether the cloud platform responds to these changes in a timely manner when the market environment or data source characteristics change. Specifically, the Delayed Association Adjustment Index is used to evaluate the speed and efficiency of the cloud platform to adjust the push strategy after detecting the change in the degree of association of the data source. If the degree of association changes rapidly, but the system adjusts slowly, the Delayed Association Adjustment Index will be high, indicating that there may be a risk of decision lag;

[0111] By calculating the correlation delay adjustment index, the cloud platform can identify the push strategy lag caused by untimely response, and take measures to improve the response speed of the decision model. A high correlation delay adjustment index value may indicate that the system's decision model needs to speed up its response. By monitoring and adjusting the correlation delay adjustment index, the cloud platform can continuously optimize the decision-making process and ensure the forward-looking and adaptable nature of the push strategy. When the market or data source characteristics change drastically, the correlation delay adjustment index provides a risk warning mechanism to help the cloud platform identify possible decision lags in advance and reduce potential market risks.

[0112] The logic for obtaining the associated delay adjustment index is as follows:

[0113] Get the data sources at different time points in the generated project push strategy, and calculate the correlation measure between data sources i and j at a specific time t. The correlation calculation formula is: , where Sim represents the similarity measurement function, and are the fuzzy feature representations of data sources i and j respectively, and the correlation change value is calculated. The calculation expression is: , where Δt represents the time interval;

[0114] Obtain the conformal prediction intervals PNEW and POLD at time t and t minus Δt, and calculate the decision adjustment range. The calculation expression is: , obtain the decision adjustment range based on data sources i and j at time t and time t-Δt , calculate the associated delay adjustment index, the calculation expression is: , where N represents the sum of the number of data source pairs.

[0115] It should be noted that in the data source preprocessing stage, all data sources are standardized and key features are extracted through feature engineering. The feature vector of each data source is fuzzified to form a fuzzy feature representation. Through the fuzzy feature representation, the cloud platform can calculate the similarity or correlation between different data sources. Common methods include calculating the overlap of fuzzy sets, cosine similarity, or calculating the correlation between predicted values ​​through machine learning models; Δt is the time interval, that is, the time step set in the analysis, which is usually set according to the data update frequency (such as daily, weekly); the correlation is obtained through data preprocessing, feature engineering and dynamic monitoring, and the decision adjustment range comes from the results of conformal prediction and fuzzy integrated analysis.

[0116] The market signal sensitivity index is an indicator used to measure the cloud platform's ability to detect and respond to changes in market signals. It reflects how quickly the cloud platform can perceive changes in the market environment and adjust its push strategy accordingly. The higher the market signal sensitivity index, the more sensitive the cloud platform is to changes in market signals and the faster it can make decisions and adjustments, thereby reducing the risks brought by market fluctuations and having an impact on the following aspects:

[0117] Evaluate decision sensitivity: The market signal sensitivity index helps evaluate the sensitivity of push strategies by measuring the cloud platform's detection and response speed to market signals, ensuring that the decision model can quickly adapt to market changes;

[0118] Risk management: The market signal sensitivity index provides an early warning mechanism to help identify the risks that may arise from changes in market signals, enabling the cloud platform to take timely countermeasures and reduce the impact of market fluctuations on investment and financing decisions.

[0119] Optimize strategy response: By analyzing the market signal sensitivity index, the cloud platform can identify the links that are slow to respond to market signals, thereby optimizing the strategy response speed and avoiding decision delays caused by lagging perception of market signals.

[0120] The logic of obtaining the market signal sensitivity index is as follows:

[0121] Get the value of market signal k at time T and calculate the change strength of market signal. The calculation expression is: , where represents the value of the market signal k at time T, is the time interval, K is the total number of market signals;

[0122] After the market signal k changes, obtain the time delay for the cloud platform to detect and adjust the strategy , calculate the response time of market signal changes, the calculation expression is: , calculate the strategy to adjust the fitness value, the calculation expression is: , calculate the market signal sensitivity index, the calculation expression is: .

[0123] It should be noted that market signals are specific data points or indicators in the market, which can reflect a certain aspect or trend of the current market. Different k represents different types of market signals, for example, macroeconomic indicators such as GDP growth rate, industry-specific indicators such as industry sales growth rate, inventory level, raw material prices, etc.; the acquisition of market signals depends on the market data sources monitored by the cloud platform, which can be public economic data, financial market data, industry analysis reports, company financial statements, or intelligence services specifically for investment and financing decision-making. The selected market signals are input into the cloud platform, and then the cloud platform's response speed and accuracy to these signals are evaluated through indicators such as the market signal sensitivity index, so as to optimize push decisions.

[0124] The obtained correlation delay adjustment index and market signal sensitivity index are calculated jointly to obtain the push stability coefficient, which is calibrated as , based on the formula: , where , are the preset proportional coefficients of the correlation delay adjustment index and the market signal sensitivity index, respectively, and , All are greater than 0;

[0125] It should be noted that the formula is obtained by technicians in this field by collecting multiple groups of sample data and setting corresponding preset proportional coefficients for each group of sample data. The specific calculation process is: substituting the obtained correlation delay adjustment index and market signal sensitivity index into the formula to form an equation group, and then screening and averaging the calculated preset proportional coefficients to obtain the preset proportional coefficients. Since each group of sample data is different, the specific values ​​of the preset proportional coefficients under different sample data are also different.

[0126] From the formula, we can know that the smaller the correlation delay adjustment index is, the larger the market signal sensitivity index is, that is, the larger the performance value of the push stability coefficient is, the push strategy can not only respond quickly to market signals, but also avoid the lag caused by changes in correlation. The cloud platform's project push decision-making in industrial and commercial investment and financing is very stable and reliable in the market environment. The cloud platform can quickly capture market signals and adjust the push strategy in time. At the same time, the decision model is very sensitive to changes in the correlation of data sources. The push decision has a high degree of foresight and adaptability, which can effectively avoid the risks brought by market fluctuations and ensure the success rate of investment and financing push;

[0127] The smaller the associated delay adjustment index and the market signal sensitivity index, that is, the smaller the performance value of the push stability coefficient, indicates that the push strategy may not be able to respond to market signals in real time. The decision-making lag causes the system to be unstable, the effect of investment and financing push is not ideal, and the response to market signal changes is slow. The push strategy cannot be adjusted in time. At the same time, there is a lag in the response of the decision-making model. The push decision may not be able to fully adapt to changes in the market environment. There is a high decision-making risk, which may lead to push failure or decline in returns.

[0128] When implementing investment and financing push strategies on the cloud platform, the push stability coefficient can be used to determine the stability and reliability of the current strategy. In order to further optimize push decisions, the generated push stability coefficient can be compared with the preset push control threshold. Different operation signals are generated based on the comparison results. These signals can be used to automate the decision-making process and guide the system to take corresponding actions, such as adjusting the push strategy, issuing warnings, or suspending push.

[0129] The generated push stability coefficient is compared with the push control threshold, and different signals are generated according to the comparison results. The different signals are respectively generating a project push strategy control signal and a project push strategy stability signal;

[0130] After obtaining the generated push stability coefficient, the generated push stability coefficient is compared with the push control threshold. If the push stability coefficient is greater than the push control threshold, a stable signal of the project push strategy is generated, which indicates that the current cloud platform push strategy shows high stability in the market environment, responds promptly to market signals, and the decision model can quickly adapt to changes in the correlation of data sources, avoiding lag effects. This means that the current push strategy can effectively respond to dynamic changes in the market, has low risks, high reliability, and no additional push strategy adjustments are required.

[0131] If the push stability coefficient is less than or equal to the push control threshold, a project push strategy control signal is generated, indicating that the current push strategy has low stability in the market environment and there are unstable factors. It may not be able to effectively respond to market changes, resulting in an increased execution risk of the push strategy. Generating a project push strategy control signal means that the current push strategy faces higher market risks and may not be able to effectively respond to market fluctuations. The uncertainty of push results increases, and push failures or market losses may occur.

[0132] The generation of regulatory signals indicates that the current market environment may have changed. The cloud platform needs to re-evaluate the correlation between market signals and data sources to update push strategies and reduce potential risks.

[0133] After the project push strategy control signal is generated, the current push strategy is regulated or adjusted. Possible control measures include:

[0134] Slow down the pace of push notifications, reduce the volume of push notifications, or delay push notifications to reduce risk exposure;

[0135] Adjust strategy parameters, re-evaluate market signals and correlations, and adjust the parameters of the decision model to improve the adaptability of the strategy; suspend push notifications. If the risk is high, it is recommended to temporarily stop push notifications and restart the push notification strategy after market conditions improve;

[0136] It should be noted that the relevant threshold information in this embodiment is pre-set by professionals and will not be explained in detail here.

[0137] The present invention provides an efficient method for optimizing the push strategy of industrial and commercial investment and financing. The method obtains and processes multi-source heterogeneous data, performs standardization processing and unified format to generate a consistent data set, thereby ensuring the consistency and comparability of the data. The standardized data set is further subjected to feature extraction and fuzzification processing to extract key features reflecting market dynamics. The method constructs a correlation matrix between data sources, triggers an update mechanism of a correlation model according to the correlation score, and uses an adaptive update algorithm to adjust the correlation matrix in real time, thereby ensuring that the correlation between data sources always reflects the current market environment and data characteristics, thereby improving the accuracy and adaptability of push decisions.

[0138] In addition, by calculating the inconsistency scores of data points, constructing conformal prediction intervals, and combining fuzzy reasoning functions to integrate and analyze multi-source data, the push decision matrix is ​​effectively generated, and the decision boundary of the push strategy is optimized using Bayesian reasoning. The strategy is dynamically adjusted to adapt to market changes, and the push effect is optimized through real-time monitoring. Finally, during the project push process, different operation signals are generated through real-time monitoring and analysis of push change information to ensure that the cloud platform can adjust the push strategy in time to cope with dynamic changes in the market, thereby greatly improving the stability, accuracy and adaptability of the cloud platform's push strategy in a complex market environment, effectively reducing investment and financing risks, and improving the project's return on investment.

[0139] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0140] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0141] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0142] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A data management cloud platform and project push method for industrial and commercial investment and financing, characterized in that: The steps include: Acquire multi-source heterogeneous data in industrial and commercial investment and financing scenarios, perform standardization and format unification to generate consistent data sets, and perform feature extraction and fuzzification on the standardized data sets; Construct a correlation matrix between data sources, trigger the update mechanism of the correlation model according to the correlation score, and adjust the correlation matrix through an adaptive update algorithm; Conformal prediction intervals are constructed based on the inconsistency scores of data points, and integrated analysis of multi-source data is performed through fuzzy reasoning functions. The push decision matrix is ​​generated by combining fuzzy features and correlation matrix. Use Bayesian reasoning to optimize the decision boundary of the push strategy, and dynamically adjust the push strategy by combining prior information with current market observation data; Conduct real-time monitoring and analysis on the push results after decision boundary optimization, obtain and analyze the push change information generated during the project push process, generate different signals based on the analysis results, and adjust the cloud platform push strategy.

2. The data management cloud platform and project push method for industrial and commercial investment and financing according to claim 1, characterized in that: Obtain multi-source heterogeneous data in industrial and commercial investment and financing scenarios, perform standardization and format unification to generate consistent data sets, and perform feature extraction and fuzzy processing on the standardized data sets, including: Standardize multi-source heterogeneous data, including timestamp alignment, unit conversion, data type conversion, and format unification; Perform feature extraction and fuzzification processing on the standardized data; Use principal component analysis or independent component analysis to extract key features, including cash flow, debt-to-asset ratio, and market share; The extracted features are converted into fuzzy sets by applying fuzzy membership functions.

3. The data management cloud platform and project push method for industrial and commercial investment and financing according to claim 2 is characterized in that: Construct a correlation matrix between data sources, trigger the update mechanism of the correlation model according to the correlation score, and adjust the correlation matrix through an adaptive update algorithm. The specific steps include: Map multi-source heterogeneous data into a high-dimensional feature space and perform fuzzy processing on each feature dimension; Calculate the membership differences between different data points on each fuzzy set as the association score; Construct a correlation matrix between data sources and compare it with the correlation threshold to determine the correlation model and automatic update trigger; When the correlation score between two data sources is lower than the correlation threshold, the update mechanism of the correlation model is automatically triggered; After the association update mechanism is triggered, the cloud platform uses an adaptive update algorithm to reconstruct the association model between data sources by adjusting the inconsistency score and fuzzy set membership.

4. The data management cloud platform and project push method for industrial and commercial investment and financing according to claim 3 is characterized in that: Conformal prediction intervals are constructed based on the inconsistency scores of data points. The integrated analysis of multi-source data is performed through fuzzy inference functions. The push decision matrix is ​​generated by combining fuzzy features and correlation matrix. The specific steps are as follows: Conformal prediction and fuzzy integrated analysis of multi-source heterogeneous data; Calculate the inconsistency score for each data point, and compare the inconsistency scores of the data points to determine the upper and lower limits of the conformal prediction interval for each data point; Determine the lower and upper bounds of the conformal prediction interval based on the confidence level; A fuzzy rule base is constructed according to different data sources and conformal prediction intervals, and the fuzzy membership of each conformal prediction interval is calculated according to the constructed fuzzy rule base; Bringing the fuzzy membership into the fuzzy reasoning function generates a decision output as the fuzzy reasoning result; All fuzzy reasoning results are merged into a push strategy, and all fuzzy reasoning outputs are combined to form a push decision matrix through fuzzy logic and optimization algorithms as the actual push strategy for investment and financing projects.

5. The data management cloud platform and project push method for industrial and commercial investment and financing according to claim 4 is characterized in that: Use Bayesian reasoning to optimize the decision boundary of the push strategy, and dynamically adjust the push strategy by combining prior information with current market observation data, including: The cloud platform integrates prior information from historical data and expert knowledge, including the company's historical performance, market volatility, and industry trends, and quantifies the prior information into prior probability distribution; Introducing observational data, which includes current market conditions, real-time financial data of enterprises, and feedback from investors; Calculate the likelihood function of the observed data and modify the prior probability distribution based on the likelihood function; By combining the prior probability distribution with the likelihood function through the Bayesian formula, the posterior probability distribution is calculated; According to the posterior probability distribution, the cloud platform dynamically adjusts the decision boundary of the push strategy.

6. The data management cloud platform and project push method for industrial and commercial investment and financing according to claim 5, characterized in that: Perform real-time monitoring and analysis on the push results after decision boundary optimization, obtain and analyze the push change information generated during the project push process. The specific steps are as follows: Obtain push change information generated during project push, including associated response information and market adaptation information; The correlation response information includes the correlation delay adjustment index, and the market adaptation information includes the market signal sensitivity index: The correlation delay adjustment index and the market signal sensitivity index are combined to generate the push stability coefficient; The correlation delay adjustment index is inversely proportional to the push stability coefficient, and the market signal sensitivity index is directly proportional to the push stability coefficient.

7. The data management cloud platform and project push method for industrial and commercial investment and financing according to claim 6, characterized in that: Generate different signals based on the analysis results and adjust the cloud platform push strategy. The specific steps include: Compare the generated push stability coefficient with the push control threshold; If the push stability coefficient is greater than the push control threshold, a project push strategy stability signal is generated, and no additional push strategy adjustment is required; If the push stability coefficient is less than or equal to the push control threshold, a project push strategy control signal is generated, the cloud platform re-evaluates the correlation between the market signal and the data source, and updates the push strategy.

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