Intelligent risk warning platform and method for enterprise decision-making

Abstract

The present application provides an intelligent risk warning platform and method for enterprise decision-making, which relates to the field of risk warning technology, including: a data acquisition module for data acquisition; an operation planning module for generating enterprise decisions; a time series analysis module for introducing a user scenario parameter set and performing time series analysis on enterprise decisions; a risk prediction module for building a risk prediction model, calculating enterprise decisions, and obtaining multiple risk coefficients; a risk warning module for interpreting risks, generating risk warning instructions, and synchronizing to an intelligent assessment terminal for risk warning. This application can solve the technical problem in the prior art that it is difficult to accurately predict long-term risks due to the fact that enterprise risk warnings ignore the evaluation of changes in decision results over time in rapid market changes or emergencies. By performing time series analysis on enterprise decisions and combining multi-dimensional risk factors for risk warning, the accuracy of long-term risk prediction is improved.

Description

Technical Field

[0001] The present application relates to the field of risk warning technology, and in particular to an intelligent risk warning platform and method for enterprise decision-making. Background Art

[0002] When businesses face multiple uncertainties such as market fluctuations, legal compliance, and technological development, implementing risk early warning systems can provide timely risk alerts and help them respond effectively. Risk early warning systems for corporate decision-making aim to help companies reduce losses and improve the accuracy and robustness of their decisions by identifying, assessing, and addressing potential risks. While existing risk early warning systems for corporate decision-making have made some progress, they still face technical challenges. Traditional risk assessment models are mostly static, based on fixed assumptions and historical data, and fail to fully account for changes over time. For example, a decision may perform well in the short term, but due to various factors, it may harbor significant potential risks in the long term. These systems lack the ability to adapt to dynamic market demands, especially during rapidly changing markets or when faced with unexpected events. Unexpected events can significantly impact a company's operations and decision-making outcomes, often fraught with uncertainty and complexity. Existing risk early warning systems struggle to effectively predict and assess these dynamic events.

[0003] In summary, the existing technology has a technical problem in that the enterprise risk warning does not adequately assess the temporal changes in decision-making results when facing rapid market changes or emergencies, resulting in inaccurate predictions of long-term risks. Summary of the Invention

[0004] The purpose of this application is to provide an intelligent risk warning platform and method for enterprise decision-making, so as to solve the technical problem in the existing technology that enterprise risk warning does not adequately assess the temporal changes in decision-making results when facing rapid market changes or emergencies, resulting in inaccurate predictions of long-term risks.

[0005] In view of the above problems, this application provides an intelligent risk warning platform and method for enterprise decision-making.

[0006] In the first aspect, the present application provides an intelligent risk warning platform for enterprise decision-making, wherein the intelligent risk warning platform for enterprise decision-making includes: a data acquisition module, the data acquisition module is used to collect data based on the enterprise's internal data source to obtain the enterprise's historical data set; an operation planning module, the operation planning module is used to perform enterprise operation planning according to the enterprise's historical data set and generate enterprise decisions; a time series analysis module, the time series analysis module is used to introduce a user scenario parameter set, perform time series analysis on the enterprise decision, and generate a decision time series data set; a risk prediction module, the risk prediction module is used to construct a risk prediction model, calculate the enterprise decision through the risk prediction model combined with the decision time series data set, and obtain multiple risk coefficients; a risk warning module, the risk warning module is used to interpret the risk based on the multiple risk coefficients combined with the enterprise decision, generate risk warning instructions, and synchronize the risk warning instructions to the intelligent assessment terminal to perform risk warning on the enterprise decision.

[0007] On the second aspect, the present application also provides an intelligent risk warning method for enterprise decision-making, wherein the intelligent risk warning method for enterprise decision-making includes: collecting data based on the enterprise's internal data source to obtain the enterprise's historical data set; conducting enterprise operation planning according to the enterprise's historical data set to generate enterprise decisions; introducing a user scenario parameter set to perform time series analysis on the enterprise decision to generate a decision time series data set; constructing a risk prediction model, calculating the enterprise decision through the risk prediction model in combination with the decision time series data set to obtain multiple risk coefficients; interpreting the risk based on the multiple risk coefficients in combination with the enterprise decision to generate a risk warning instruction, and synchronizing the risk warning instruction to the intelligent assessment terminal to perform risk warning on the enterprise decision.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] Through the data acquisition module, the data acquisition module is used to collect data based on the enterprise's internal data source to obtain the enterprise's historical data set; the operation planning module is used to perform enterprise operation planning according to the enterprise's historical data set and generate enterprise decisions; the time series analysis module is used to introduce the user scenario parameter set, perform time series analysis on the enterprise decision, and generate a decision time series data set; the risk prediction module is used to build a risk prediction model, calculate the enterprise decision through the risk prediction model combined with the decision time series data set to obtain multiple risk coefficients; the risk warning module is used to interpret the risk based on the multiple risk coefficients combined with the enterprise decision, generate risk warning instructions, and synchronize the risk warning instructions to the intelligent assessment terminal to issue risk warnings for the enterprise decision. In other words, by performing time series analysis on enterprise decisions, capturing the performance of decisions in different time periods, especially the risks in the face of rapid changes or emergencies, integrating multi-dimensional risk factors to interpret the risks of enterprise decisions, generating warning instructions for real-time warnings and feedback, and improving the accuracy of long-term risk prediction.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0012] Figure 1 This is a schematic diagram of the structure of the intelligent risk warning platform used for enterprise decision-making in this application;

[0013] Figure 2 This is a flow chart of the intelligent risk warning method used in this application for enterprise decision-making.

[0014] Explanation of the accompanying drawings: data acquisition module 11, operation planning module 12, time series analysis module 13, risk prediction module 14, risk warning module 15. DETAILED DESCRIPTION

[0015] This application addresses the existing technical problem of inaccurate long-term risk predictions due to insufficient assessment of the temporal changes in decision-making results when facing rapid market changes or emergencies by providing an intelligent risk warning platform and method for enterprise decision-making. By performing temporal analysis on enterprise decisions, capturing the performance of decisions in different time periods, especially the risks faced by rapid changes or emergencies, integrating multi-dimensional risk factors to interpret the risks of enterprise decisions, and generating early warning instructions for real-time warning and feedback, the accuracy of long-term risk predictions is improved.

[0016] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0017] For example 1, please refer to the attached Figure 1 The present application provides an intelligent risk warning platform for enterprise decision-making, wherein the intelligent risk warning platform for enterprise decision-making is used to implement the steps of an intelligent risk warning method for enterprise decision-making, and the intelligent risk warning platform for enterprise decision-making includes:

[0018] The data collection module 11 is used to collect data based on the enterprise's internal data source to obtain the enterprise's historical data set.

[0019] Specifically, various data sources are collected from multiple internal enterprise data sources to create a historical enterprise dataset. This includes data from finance, marketing, and operations departments, such as financial statements and market reports. Internal enterprise data sources refer to the various types of data generated by various departments within the enterprise. For example, financial companies need to collect data including, but not limited to, financial indicators (such as revenue, profit, and cash flow), market performance (such as stock prices and market indices), operational data (such as production volume and supply chain status), and external economic data (such as macroeconomic indicators and industry data). This meticulous data collection builds a comprehensive and accurate historical dataset, helping enterprises identify potential risk points.

[0020] The operation planning module 12 is used to perform enterprise operation planning according to the enterprise historical data set and generate enterprise decisions.

[0021] Specifically, using a company's historical data sets to develop an operational plan involves analyzing historical data, identifying trends and patterns, and setting future goals and strategies based on the analysis, including but not limited to financial targets, market expansion plans, and production strategies. Based on the operational plan, specific decisions are made, including investment decisions, market strategy adjustments, new product development, and production plans. For example, suppose a retail company collects historical sales data, inventory data, and customer preference data. During operational planning, data analysis reveals that sales of Product A increase during the winter. Based on this trend, the company decides to increase inventory of this product in the fall and winter, expand production lines, and develop targeted marketing strategies. Simultaneously, data indicates a growing trend in online sales of Product A, leading to a decision to increase investment in e-commerce platforms. Analyzing historical data and predicting future market trends and demand helps companies optimize their operational strategies and make more forward-looking decisions.

[0022] The time series analysis module 13 is used to introduce a user scenario parameter set, perform time series analysis on the enterprise decision, and generate a decision time series data set.

[0023] Specifically, user behavior is analyzed to identify user behavior patterns and preferences. These patterns are then combined with specific business scenarios to determine user needs in specific scenarios. When analyzing user behavior and preference information, multiple timestamp data sets are extracted to determine user behavior preferences at different time points. For example, a user may express interest in a certain product or service within a specific time period. Timestamp data can be used to capture the dynamic changes in user behavior over time. Based on timestamp information, user needs are mined to identify specific needs at different times, locations, or scenarios. The mined user usage scenario information is integrated into a user scenario parameter set. Each user usage scenario in the user scenario parameter set is traversally matched with enterprise decisions. Based on the timestamp data in the user scenario parameter set, the enterprise decisions are analyzed over time to capture their temporal characteristics, understand their changes over time, and their relationship to user needs. Enterprise decisions are serialized and arranged in a specific order (such as ascending or descending) to form a decision time series dataset. This decision time series dataset provides a comprehensive view of enterprise decisions over time, helping enterprises record the evolution of their decision effects. For example, whether a decision remains effective in the long term or is affected by changes in the external environment. By introducing user scenario parameter sets, conducting time-series analysis on enterprise decisions and generating a decision time-series dataset, we can combine user behavior and needs with the time dimension of enterprise decisions, understand the changing trends of decisions over time, identify the long-term impact or lag effects of decisions, and better understand the dynamic performance of decisions.

[0024] The risk prediction module 14 is used to construct a risk prediction model, calculate the enterprise decision through the risk prediction model in combination with the decision time series data set, and obtain multiple risk coefficients.

[0025] Specifically, preprocessing is performed on information such as the company's historical data, current decision-making processes, and external environmental variables. This includes data cleaning, normalization or standardization, handling missing values, and data set partitioning. For example, the company's financial and market data should be converted into a format suitable for LSTM input, such as normalizing to a range between 0 and 1. Appropriate deep learning models, such as RNNs (recurrent neural networks) and LSTMs (long short-term memory networks), are selected for handling risk prediction problems involving time series. For example, designing the LSTM network architecture involves determining the number of layers, the number of neurons per layer, the activation function, the loss function, and the optimizer. The input layer receives normalized time series data, the LSTM layer extracts time series features, and the output layer predicts risk. The LSTM model is trained using a training dataset. The preprocessed company historical dataset is fed into the LSTM model, and model parameters are adjusted through multiple iterations until the model performs as expected on the training data. A portion of company data not previously used in training is fed into the LSTM model to evaluate the model's predictive accuracy and generalization ability, and the model parameters are adjusted for optimal performance. The trained model is then deployed in real-world applications to predict risks for corporate decisions.

[0026] Risk prediction models are used to analyze corporate decisions and identify risk factors influencing them. Ensemble learning techniques are used to extract more precise risk variables from these risk factors. Based on a constructed data mapping network, the multiple risk variables obtained through ensemble learning are correlated with the company's decision-making time series dataset. By analyzing the correlations between risk variables and decision data, potential risk patterns and risk triggers are identified. The magnitude of changes in risk variables over time and market conditions is analyzed to identify the volatility of risk factors across different time periods and scenarios. Multiple extremes of volatility—points where risk factors reach extremes—are identified, potentially signaling the onset of risk or significant changes in decision outcomes. Sensitivity analysis is performed on the decision-making time series dataset to assess the impact of extremes on decisions and determine the magnitude of the impact of each risk factor on the outcome. Through multidimensional data and historical time series analysis, potential risks are identified and their impact quantified. Different risk factors can clarify which decision-making processes are most susceptible to risk, allowing companies to prioritize high-risk areas and reduce decision uncertainty and potential losses.

[0027] The risk warning module 15 is used to interpret the risk based on the multiple risk coefficients and the enterprise decision, generate a risk warning instruction, and synchronize the risk warning instruction to the intelligent assessment terminal to issue a risk warning for the enterprise decision.

[0028] Specifically, risk factors are graded and converted into a risk level list. This risk level list can be categorized into low, medium, and high levels using thresholds. Based on this generated risk level list, the company's decision-making processes are traversed and the corresponding risk levels are assigned to each decision based on its risk factors. Each decision may involve multiple risk factors (such as market, financial, and operational), and the combined performance of these risk factors determines the overall risk level of the decision. For example, a decision with a market risk factor of 0.8 and a supply chain risk factor of 0.5 would be classified as "medium-high risk." Historical risk logs are used to analyze past risks encountered in corporate decisions and, based on these historical records, set risk warning thresholds. The risk warning threshold indicates that when the risk level of a decision reaches or exceeds this threshold, the company needs to take additional preventative measures. The corresponding risk level of the decision is compared with the risk warning threshold. If the risk level of a decision is greater than or equal to the warning threshold, the warning mechanism is triggered and the corresponding risk warning instructions are generated.

[0029] Risk warning instructions are synchronized to the company's intelligent assessment terminal, which parses the instructions and identifies the specific risk type they address. Based on the identified risk warning type and the company's actual decision-making, appropriate risk response measures are formulated. Risk response measures not only include action strategies but also require execution time data, which specifies the timing of initiation, completion, and tracking of risk response measures. Based on the set execution time data, the corresponding risk response measures are initiated. After execution, a risk assessment is conducted to determine whether the measures have successfully reduced the risk level. Adjustment instructions or suspension instructions are generated, and the results of the risk assessment are fed back to the intelligent assessment terminal. The intelligent terminal parses the instructions and guides the company to take action based on the specific warning type. By interpreting multi-dimensional risk factors, more accurate risk warnings are provided, preventing omissions or misjudgments. Generated risk warning instructions are synchronized to the intelligent assessment terminal in real time, ensuring that the company can take countermeasures at the first sign of risk.

[0030] Furthermore, the time series analysis module 13 in the intelligent risk warning platform for enterprise decision-making is also used to:

[0031] Conduct user behavior analysis through a user interaction platform to obtain user behavior preference information; conduct analysis based on the user behavior preference information in combination with business scenario information to obtain a user demand information set; extract multiple timestamp data based on the user behavior preference information, mine the user demand information set according to the multiple timestamp data, and determine multiple user usage scenario information, wherein the multiple timestamp data correspond to the user behavior preference information; and add the multiple user usage scenario information to the user scenario parameter set.

[0032] Specifically, user behavior data is collected through user interaction platforms (such as social media, online forums, customer service systems, etc.) and analyzed to identify user preferences, including user preferences for products or services, purchasing habits, interaction patterns, etc. User behavior analysis refers to the systematic study of user behavior (such as clicks, purchases, comments, etc.) on interactive platforms to understand user behavior patterns and preferences. User behavior preference information is combined with specific business scenarios to understand user needs, expectations, and behavior patterns in specific business scenarios, and analyze and determine user needs. Timestamp information is extracted from user behavior data. The timestamp data is associated with the user's behavior preference information, recording the time point when the user behavior occurred, and providing a time dimension for analyzing user behavior patterns. For example, an e-commerce website will analyze users' shopping behavior in different time periods (such as holidays and promotional events) to identify users' shopping needs and preferences at these specific time points.

[0033] Mining user demand information sets based on timestamp data identifies user usage scenarios at different points in time. The mined user usage scenario information is added to the user scenario parameter set, a collection of various parameters and information used to describe and simulate user behavior and needs, including personal characteristics, behavior patterns, preferences, and requirements. By analyzing user behavior preferences and accurately understanding user needs in different scenarios, companies can provide more personalized services and product recommendations.

[0034] Furthermore, the time series analysis module 13 in the intelligent risk warning platform for enterprise decision-making is also used to:

[0035] Based on the multiple user usage scenario information, the enterprise decision is traversed for matching, and the enterprise decision is pre-processed according to the matching results to generate multiple decision processing data; the multiple timestamp data in the user scenario parameter set are standardized to generate multiple standard timestamp data; the multiple decision processing data are time-series mapped according to the multiple standard timestamp data to generate a data mapping network; the multiple decision processing data are serialized based on the data mapping network to determine the decision time series data set.

[0036] Specifically, multiple user usage scenario information is traversally matched with enterprise decisions. This involves comparing actual user usage with enterprise business decisions to determine the degree of alignment between the decisions and user needs and behavior patterns. Based on the matching results, decisions are pre-processed, and data is cleansed (identified, corrected, deleted) or supplemented (filled, amended) to ensure the integrity and reliability of the data used in enterprise decisions, resulting in multiple decision-processing data. Multiple timestamp data is standardized, converting multiple timestamp data within a user scenario parameter set into a standard format with the same time resolution. Timestamp data typically contains date and time information and may be stored in different formats and precisions. Standardizing this data means converting it to a uniform format and precision for easier analysis and comparison. Standard timestamp data has the same time resolution, meaning a uniform time unit such as seconds, minutes, or hours.

[0037] Based on multiple standard timestamp data, multiple decision processing data are time-series mapped, each decision processing data is arranged in chronological order and associated with the corresponding standard timestamp data to construct a time-based decision data network. A data mapping network is a network structure consisting of data points and their timestamps, used to represent data changes over time. Based on the generated data mapping network, multiple decision processing data are serialized, arranging them in a specific order (such as ascending or descending) to ensure that the data has a clear sequential relationship. By matching user usage scenarios with enterprise decisions, enterprises can ensure that their decisions are consistent with actual user needs and behaviors. Decision processing data is organized in chronological order to form an ordered dataset, providing enterprises with a historical perspective on decision-making and helping to predict future trends and potential risks.

[0038] Furthermore, the risk prediction module 14 in the intelligent risk warning platform for enterprise decision-making is also used to:

[0039] The enterprise decision is simulated and executed, and risk analysis is performed in combination with the risk prediction model to determine multiple risk factors; integrated learning is performed based on the multiple risk factors to obtain multiple risk variables; according to the data mapping network, the multiple risk variables are associated with the decision time series data set and analyzed to obtain multiple risk association data; fluctuation calculation is performed based on the multiple risk association data to determine multiple fluctuation extremes; sensitivity analysis is performed on the decision time series data set according to the multiple fluctuation extremes to obtain the multiple risk coefficients.

[0040] Specifically, the process of executing corporate decisions is simulated in a virtual environment, the resulting outcomes are observed, and risk prediction models are used to analyze the simulation results. This identifies the risk factors influencing corporate decisions, including risks across various dimensions, such as finance, market, and supply chain. Ensemble learning methods (such as random forests and gradient boosting machines) are used to process and integrate multiple risk factors, extracting more accurate risk variables. Ensemble learning is a machine learning method that improves overall model accuracy by combining the predictions of multiple models. It combines multiple weak learners (such as decision trees) to generate more robust predictions. A data mapping network is used to associate risk variables with a time-series dataset of decisions. The analysis then examines how risk variables change as the dataset changes, revealing the relationship between risk and decision making.

[0041] By analyzing the temporal consistency or strength of correlation between risk variables and decision data, correlation patterns can be identified. Correlation analysis is not just a static analysis; it also requires dynamic analysis, combined with time series data. This involves identifying whether the relationship between certain risk variables and decisions changes over time. For example, suppose a company makes a market expansion decision in Q1. Subsequently, the market demand risk variable exhibits significant fluctuations. If a similar decision is made in the following quarter (Q2), it is necessary to analyze whether the market risk variable exhibits similar fluctuations at similar time points. This helps the company identify the temporal dependency between risk and decision making—that is, how the correlation between risk variables and decisions strengthens or weakens over time. Through correlation analysis, multiple risk correlation data sets are identified and extracted. Risk correlation data refers to the correlation between each risk variable and the company's decision data, typically including correlation strength and correlation patterns. For example, some decisions may lead to a rapid increase in risk in the short term, while the risk association of other decisions may be more delayed.

[0042] Analyze the changing trends of multiple risk-related data sets and identify the magnitude and frequency of fluctuations. Fluctuation data reflects the severity of risk variables over time, such as dramatic changes in market demand or supply chain fluctuations. Fluctuation calculation, based on statistical maximum and minimum calculations and local maximum / minimum detection algorithms, identifies extreme fluctuations in the data—i.e., peaks and valleys in the risk-related data—and identifies time points with the most significant risk fluctuations. This analysis uses extreme fluctuations to analyze the sensitivity of enterprise decisions to various risk factors, outputting multiple risk coefficients that quantify the impact of each risk factor on the decision. These risk coefficients indicate the vulnerability or sensitivity of enterprise decisions to different risk factors, helping enterprises prioritize their responses to different risks. For example, if the impact coefficient of market demand fluctuations on a company's pricing strategy during a certain period is 0.8, while the risk coefficient of supply chain fluctuations is 0.3, this indicates that market demand fluctuations have a significantly greater impact on enterprise decisions. Through simulation execution and ensemble learning, potential risk factors are more accurately predicted and key risk variables are generated. Fluctuation calculation and sensitivity analysis help enterprises identify extreme risk events and highly sensitive risk factors, thereby optimizing risk management and decision-making strategies.

[0043] Furthermore, the risk warning module 15 in the intelligent risk warning platform for enterprise decision-making is also used to:

[0044] Based on the multiple risk-related data, the multiple risk coefficients are divided to generate a risk level list; the risk level list is traversed and matched with the enterprise decision to determine the risk level of the enterprise decision; the historical risk record log is called, and the risk warning critical value is set according to the historical risk record log; the risk level of the enterprise decision is compared with the risk warning critical value. If the risk level of the enterprise decision is greater than or equal to the risk warning critical value, the warning mechanism is triggered to generate the risk warning instruction.

[0045] Specifically, based on multiple risk-related data, multiple risk coefficients are divided to generate a risk level list. The risk level list is a list that classifies different risk levels, usually including low risk, medium risk, and high risk levels. For example, assuming the risk coefficient ranges from 0 to 1, by setting a threshold, the risk coefficient is divided into levels: 0.0 to 0.3 is low risk, 0.3 to 0.7 is medium risk, and 0.7 and above is high risk. According to the generated risk level list, the decision-making behavior of the enterprise is traversed, and the corresponding risk level is matched according to the risk coefficient of different decisions to determine the specific risk level corresponding to the enterprise decision. The historical risk record log is called, which contains relevant information on risk events encountered by the enterprise in the past and their handling results, to obtain valuable information about risk events.

[0046] Based on data from historical risk logs, the risk warning threshold is determined based on the frequency, severity, or other relevant indicators of historical risk events, triggering risk warnings. The risk warning threshold indicates that when the risk level of a decision reaches or exceeds this threshold, the company needs to take additional preventative measures. The risk level of the company's decision is compared with the risk warning threshold. When the risk level of the decision is greater than or equal to the risk warning threshold, the risk exceeds the acceptable range, triggering the warning mechanism and generating a risk warning instruction, notifying relevant departments or individuals to take risk mitigation measures. The generated risk warning instruction informs decision-makers of specific risk factors (such as market risk and operational risk) and the necessary countermeasures (such as adjusting strategy and increasing inventory). By classifying risk factors and generating a risk level list based on risk-related data, and then combining historical risk logs to set warning thresholds, an automated risk warning mechanism is implemented. Based on historical data and risk assessments from real-time decision-making, warning instructions are triggered in real time, helping companies effectively avoid potential major risks. Companies can quickly take countermeasures for high-risk decisions and reduce potential operational, market, or financial losses.

[0047] Furthermore, the risk warning module 15 in the intelligent risk warning platform for enterprise decision-making is also used to:

[0048] The risk warning instruction is parsed by the intelligent assessment terminal to obtain a risk warning type; risk response measures are formulated according to the risk warning type and in combination with the enterprise decision, and the risk response measures have execution time data; a risk assessment is performed on the execution of the risk response measures by the enterprise decision according to the execution time data to generate an execution risk assessment result; the execution risk assessment result is fed back to the intelligent assessment terminal to issue a risk warning to the enterprise decision.

[0049] Specifically, risk warning instructions are sent to the intelligent assessment terminal, a risk management tool or platform designed to receive, analyze, and display risk warning information. The intelligent assessment terminal receives warning instructions containing specific risk information, such as the source, type, and severity of the risk. The terminal then analyzes these instructions and identifies the warning type (e.g., market risk, financial risk, supply chain risk, etc.). When the risk level of a particular enterprise decision exceeds a preset risk threshold, a risk warning instruction is generated and immediately synchronized to the intelligent assessment terminal. After receiving the instruction, the terminal analyzes the information contained in the instruction to identify the specific risk type involved. For example, the terminal can determine whether the warning was triggered by market demand fluctuations or supply chain disruptions. Based on the analyzed risk warning type and the enterprise's actual decision, appropriate risk response measures are formulated, including decision changes, additional risk control measures, and adjustments to resource allocation. A clear implementation timeline is established for each measure. Execution time data indicates when the risk response measures will be initiated, completed, and tracked. This helps enterprises clarify the implementation progress of the measures and ensures their timely execution.

[0050] Based on the pre-set execution time data, the corresponding risk response measures are executed for the enterprise's decision, and a risk assessment is conducted on their effectiveness. By comparing the risk levels before and after implementation, the system assesses whether these measures have effectively reduced risk and whether they have introduced new risks. The risk assessment results are recorded and summarized, including any actions taken to adjust or suspend the decision, the effectiveness of the risk response measures, any new risk points, and recommended follow-up actions. The assessment results are fed back to the intelligent assessment terminal, which uses this feedback to reassess the current risk level and decide whether to maintain or lift the risk alert. Furthermore, based on the assessment results, new risk alert instructions are generated to alert the enterprise to further address potential risks. Risk alert instructions are synchronized to the intelligent assessment terminal, which then analyzes them, formulates risk response measures based on the risk type, and then executes a risk assessment and feeds it back to the intelligent terminal, forming a complete risk management closed loop. This enables enterprises to quickly respond to risk alerts, implement targeted risk response measures, and adjust decisions based on risk assessment results, ensuring the effectiveness of risk management.

[0051] Furthermore, the risk warning module 15 in the intelligent risk warning platform for enterprise decision-making is also used to:

[0052] Based on the execution risk assessment result, a risk calculation is performed on the enterprise decision to execute the risk response measures to obtain multiple risk response coefficients; it is determined whether the multiple risk response coefficients are less than the multiple risk coefficients; if the multiple risk response coefficients are greater than or equal to the multiple risk coefficients, a pause instruction is generated, and the enterprise decision is paused through the pause instruction to obtain first risk warning information; if the multiple risk response coefficients are less than the multiple risk coefficients, an adjustment instruction is generated, and the enterprise decision is dynamically adjusted through the adjustment instruction in combination with the risk response measures to obtain second risk warning information; the first risk warning information or the second risk warning information is synchronized to the intelligent assessment terminal.

[0053] Specifically, based on the results of the risk assessment, a risk calculation is performed on the enterprise's decision to implement risk response measures. The effectiveness of the risk response measures after implementation is evaluated to determine whether the risk is effectively controlled. A coefficient is calculated for each risk response measure to reflect the measure's effectiveness in reducing risk. This coefficient measures the remaining risk level after the decision is adjusted, indicating whether the current risk response measures have effectively reduced the original risk coefficient. The calculated risk response coefficient after implementing the risk response measures is compared with the original risk coefficient to determine whether the risk has been effectively reduced and to determine the effectiveness of the current risk response measures. If the risk response coefficient is greater than or equal to the original risk coefficient, a pause instruction is generated, halting the enterprise's current decision execution process and generating a first risk warning message, indicating that the adjusted decision has actually exacerbated the risk and that continued execution would result in more serious consequences, thus requiring the decision to be suspended. If the risk response coefficient is less than the original risk coefficient, indicating that the risk response measures have effectively reduced the risk, an adjustment instruction is generated, allowing the enterprise to continue optimizing decision execution. A second risk warning message is also generated, informing the enterprise that the current risk has been mitigated, but that continued observation and further adjustments are still required.

[0054] Regardless of whether a pause or adjustment order is generated, the corresponding first or second risk warning information is synchronized to the intelligent assessment terminal. The intelligent terminal will analyze these warnings and guide the enterprise to take action based on the specific warning type. After the first risk warning information is synchronized to the intelligent terminal, the enterprise will receive a decision suspension notification and take countermeasures based on risk analysis (such as reassessing market strategies and adjusting supply chain plans). The second risk warning information will inform the enterprise that the risk has been alleviated, but dynamic adjustments are required. The intelligent terminal will continuously track risk changes and guide the enterprise for further optimization. By calculating the risk of the enterprise's decision after the risk response measures are implemented and comparing the risk response coefficient with the original risk coefficient, it determines whether to issue a pause or adjustment order. Based on the results, the first or second risk warning information is generated and synchronized to the intelligent assessment terminal, ensuring information transparency and timeliness, and facilitating rapid risk response.

[0055] In summary, the intelligent risk warning platform for enterprise decision-making provided by this application has the following technical effects:

[0056] Through the data acquisition module, the data acquisition module is used to collect data based on the enterprise's internal data source to obtain the enterprise's historical data set; the operation planning module is used to perform enterprise operation planning according to the enterprise's historical data set and generate enterprise decisions; the time series analysis module is used to introduce the user scenario parameter set, perform time series analysis on the enterprise decision, and generate a decision time series data set; the risk prediction module is used to build a risk prediction model, calculate the enterprise decision through the risk prediction model combined with the decision time series data set to obtain multiple risk coefficients; the risk warning module is used to interpret the risk based on the multiple risk coefficients combined with the enterprise decision, generate risk warning instructions, and synchronize the risk warning instructions to the intelligent assessment terminal to issue risk warnings for the enterprise decision. In other words, by performing time series analysis on enterprise decisions, capturing the performance of decisions in different time periods, especially the risks in the face of rapid changes or emergencies, integrating multi-dimensional risk factors to interpret the risks of enterprise decisions, generating warning instructions for real-time warnings and feedback, and improving the accuracy of long-term risk prediction.

[0057] Example 2: Based on the same inventive concept as the intelligent risk warning platform for enterprise decision-making in the aforementioned Example 1, this application also provides an intelligent risk warning method for enterprise decision-making, please refer to the attached Figure 2 , the intelligent risk warning method for enterprise decision-making includes:

[0058] Data collection is performed based on the enterprise's internal data sources to obtain an enterprise historical data set; enterprise operation planning is performed according to the enterprise historical data set to generate enterprise decisions; user scenario parameter sets are introduced to perform time series analysis on the enterprise decisions to generate a decision time series data set; a risk prediction model is constructed, and the enterprise decision is calculated through the risk prediction model in combination with the decision time series data set to obtain multiple risk coefficients; risk interpretation is performed based on the multiple risk coefficients in combination with the enterprise decision to generate risk warning instructions, and the risk warning instructions are synchronized to the intelligent assessment terminal to issue risk warnings for the enterprise decision.

[0059] Furthermore, the user scenario parameter set includes:

[0060] Conduct user behavior analysis through a user interaction platform to obtain user behavior preference information; conduct analysis based on the user behavior preference information in combination with business scenario information to obtain a user demand information set; extract multiple timestamp data based on the user behavior preference information, mine the user demand information set according to the multiple timestamp data, and determine multiple user usage scenario information, wherein the multiple timestamp data correspond to the user behavior preference information; and add the multiple user usage scenario information to the user scenario parameter set.

[0061] Furthermore, the user scenario parameter set is introduced to perform time series analysis on the enterprise decision to generate a decision time series data set, including:

[0062] Based on the multiple user usage scenario information, the enterprise decision is traversed for matching, and the enterprise decision is pre-processed according to the matching results to generate multiple decision processing data; the multiple timestamp data in the user scenario parameter set are standardized to generate multiple standard timestamp data; the multiple decision processing data are time-series mapped according to the multiple standard timestamp data to generate a data mapping network; the multiple decision processing data are serialized based on the data mapping network to determine the decision time series data set.

[0063] Furthermore, the enterprise decision is calculated by combining the risk prediction model with the decision time series data set to obtain multiple risk coefficients, including:

[0064] The enterprise decision is simulated and executed, and risk analysis is performed in combination with the risk prediction model to determine multiple risk factors; integrated learning is performed based on the multiple risk factors to obtain multiple risk variables; according to the data mapping network, the multiple risk variables are associated with the decision time series data set and analyzed to obtain multiple risk association data; fluctuation calculation is performed based on the multiple risk association data to determine multiple fluctuation extremes; sensitivity analysis is performed on the decision time series data set according to the multiple fluctuation extremes to obtain the multiple risk coefficients.

[0065] Furthermore, the risk interpretation based on the multiple risk coefficients combined with the enterprise decision and the generation of risk warning instructions include:

[0066] Based on the multiple risk-related data, the multiple risk coefficients are divided to generate a risk level list; the risk level list is traversed and matched with the enterprise decision to determine the risk level of the enterprise decision; the historical risk record log is called, and the risk warning critical value is set according to the historical risk record log; the risk level of the enterprise decision is compared with the risk warning critical value. If the risk level of the enterprise decision is greater than or equal to the risk warning critical value, the warning mechanism is triggered to generate the risk warning instruction.

[0067] Furthermore, synchronizing the risk warning instruction to the intelligent assessment terminal to issue a risk warning for the enterprise decision includes:

[0068] The risk warning instruction is parsed by the intelligent assessment terminal to obtain a risk warning type; risk response measures are formulated according to the risk warning type and in combination with the enterprise decision, and the risk response measures have execution time data; a risk assessment is performed on the execution of the risk response measures by the enterprise decision according to the execution time data to generate an execution risk assessment result; the execution risk assessment result is fed back to the intelligent assessment terminal to issue a risk warning to the enterprise decision.

[0069] Furthermore, feeding back the execution risk assessment result to the intelligent assessment terminal to provide risk warning for the enterprise decision-making includes:

[0070] Based on the execution risk assessment result, a risk calculation is performed on the enterprise decision to execute the risk response measures to obtain multiple risk response coefficients; it is determined whether the multiple risk response coefficients are less than the multiple risk coefficients; if the multiple risk response coefficients are greater than or equal to the multiple risk coefficients, a pause instruction is generated, and the enterprise decision is paused through the pause instruction to obtain first risk warning information; if the multiple risk response coefficients are less than the multiple risk coefficients, an adjustment instruction is generated, and the enterprise decision is dynamically adjusted through the adjustment instruction in combination with the risk response measures to obtain second risk warning information; the first risk warning information or the second risk warning information is synchronized to the intelligent assessment terminal.

[0071] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1The intelligent risk warning platform for enterprise decision-making and the specific examples in Example 1 are also applicable to the intelligent risk warning method for enterprise decision-making in this embodiment. Through the detailed description of the intelligent risk warning platform for enterprise decision-making, those skilled in the art can clearly understand the intelligent risk warning method for enterprise decision-making in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the method disclosed in the embodiment, since it corresponds to the platform disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the platform part description.

[0072] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0073] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. An intelligent risk warning platform for enterprise decision-making, characterized by: include: A data collection module, which is used to collect data based on the enterprise's internal data sources to obtain the enterprise's historical data set; An operation planning module, the operation planning module is used to perform enterprise operation planning according to the enterprise historical data set and generate enterprise decisions; A time series analysis module, which is used to introduce a user scenario parameter set, perform time series analysis on the enterprise decision, and generate a decision time series data set; A risk prediction module, which is used to construct a risk prediction model, calculate the enterprise decision by combining the risk prediction model with the decision time series data set to obtain multiple risk coefficients; A risk warning module, configured to interpret the risk based on the multiple risk factors and the enterprise decision, generate a risk warning instruction, and synchronize the risk warning instruction to the intelligent assessment terminal to provide a risk warning for the enterprise decision; The timing analysis module is used for: Conduct user behavior analysis through user interaction platforms to obtain user behavior preference information; Analyze the user behavior preference information in combination with the business scenario information to obtain a user demand information set; Extracting multiple timestamp data based on the user behavior preference information, mining the user demand information set according to the multiple timestamp data, and determining multiple user usage scenario information, wherein the multiple timestamp data correspond to the user behavior preference information; Adding the plurality of user usage scenario information to the user scenario parameter set; The timing analysis module is used for: Traversing the enterprise decisions for matching based on the plurality of user usage scenario information, pre-processing the enterprise decisions according to the matching results, and generating a plurality of decision processing data; Standardizing the multiple timestamp data in the user scenario parameter set to generate multiple standard timestamp data; Performing time series mapping on the plurality of decision processing data according to the plurality of standard time stamp data to generate a data mapping network; The plurality of decision processing data are serialized based on the data mapping network to determine the decision time series data set.

2. The intelligent risk warning platform for enterprise decision-making according to claim 1, characterized in that: The risk prediction module is used to: Simulate and execute the enterprise decision, perform risk analysis in combination with the risk prediction model, and determine multiple risk factors; Performing integrated learning based on the multiple risk factors to obtain multiple risk variables; performing correlation analysis on the plurality of risk variables and the decision time series data set according to the data mapping network to obtain a plurality of risk correlation data; Fluctuation calculation is performed based on the multiple risk-related data to determine multiple fluctuation extreme values, and sensitivity analysis is performed on the decision time series data set according to the multiple fluctuation extreme values to obtain the multiple risk coefficients.

3. The intelligent risk warning platform for enterprise decision-making according to claim 2, characterized in that: The risk warning module is used to: Dividing the plurality of risk factors based on the plurality of risk association data to generate a risk level list; Traversing the risk level list and matching it with the enterprise decision to determine the risk level of the enterprise decision; Calling historical risk record logs and setting risk warning thresholds based on the historical risk record logs; The risk level of the enterprise decision is compared with the risk warning critical value. If the risk level of the enterprise decision is greater than or equal to the risk warning critical value, the warning mechanism is triggered and the risk warning instruction is generated.

4. The intelligent risk warning platform for enterprise decision-making according to claim 1, characterized in that: The risk warning module is used to: Parsing the risk warning instruction through the intelligent assessment terminal to obtain the risk warning type; Formulate risk response measures according to the risk warning type and in combination with the enterprise decision, wherein the risk response measures have execution time data; Performing a risk assessment on the enterprise decision to execute the risk response measure according to the execution time data, and generating an execution risk assessment result; The execution risk assessment result is fed back to the intelligent assessment terminal to provide risk warning for the enterprise decision.

5. The intelligent risk warning platform for enterprise decision-making according to claim 4, characterized in that: The risk warning module is used to: performing risk calculation on the enterprise decision to execute the risk response measure based on the execution risk assessment result to obtain a plurality of risk response coefficients; determining whether the plurality of risk response coefficients are less than the plurality of risk coefficients; If the multiple risk response coefficients are greater than or equal to the multiple risk coefficients, a pause instruction is generated, and the enterprise decision is suspended through the pause instruction to obtain first risk warning information; If the multiple risk response coefficients are smaller than the multiple risk coefficients, an adjustment instruction is generated, and the enterprise decision is dynamically adjusted by combining the adjustment instruction with the risk response measures to obtain second risk warning information; The first risk warning information or the second risk warning information is synchronized to the intelligent assessment terminal.

6. An intelligent risk warning method for enterprise decision-making, characterized by: Executed by the intelligent risk warning platform for enterprise decision-making according to any one of claims 1 to 5, the intelligent risk warning method for enterprise decision-making comprises: Collect data based on the company's internal data sources to obtain the company's historical data set; Perform enterprise operation planning according to the enterprise historical data set and generate enterprise decisions; Introducing a user scenario parameter set, performing time series analysis on the enterprise decision, and generating a decision time series data set; Constructing a risk prediction model, and calculating the enterprise decision by combining the risk prediction model with the decision time series data set to obtain multiple risk coefficients; Based on the multiple risk coefficients and the enterprise decision, a risk interpretation is performed to generate a risk warning instruction, and the risk warning instruction is synchronized to the intelligent assessment terminal to issue a risk warning for the enterprise decision.

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