Tourism company financial risk evaluation system

Abstract

The invention relates to the technical field of financial risks, in particular to a tourism company financial risk evaluation system, which comprises a data acquisition module for acquiring and integrating internal and external multi-source data and supporting dynamic risk evaluation and early warning; the mixed algorithm evaluation module is fused with multiple models to dynamically evaluate the risk level and quantify the financial risk probability; the dynamic early warning response module identifies risk signals in real time, triggers multi-stage early warning and visualizes a conduction path; the intelligent decision support module is matched with the risk disposal scheme library and provides corresponding decisions; and the self-learning optimization module is used for evaluating parameters through case iteration optimization. According to the method, information collection and standardization are more comprehensive, a self-learning optimization module is added, a tourism company can conveniently balance the cost and the long-term influence of customer loss to make corresponding strategy adjustment by constructing a digital twin sandbox and simulating the financial influence of a rehearsal decision, and strategy hedging risks are added through measures such as customer reservation excitation.

Description

Technical Field

[0001] The present invention relates to the technical field of financial risks, and particularly to a financial risk evaluation system for a tourism company. Background Art

[0002] Financial risk includes the risk that an enterprise may lose its debt-servicing ability and the variability of shareholders' returns. As the proportion of debt, lease, and preferred stock financing in the enterprise's capital structure increases, the fixed costs of the enterprise will increase, and as a result, the possibility of the enterprise losing its cash solvency also increases. The financial risks of tourism companies are complex and interactive, and need to be comprehensively addressed from multiple dimensions such as capital structure optimization, technological empowerment, and policy coordination. Small and medium-sized enterprises especially need to strengthen their internal management capabilities, while large enterprises should focus on the balance between long-term debt and cash flow.

[0003] Currently, with the complexity of the tourism market environment, traditional models such as linear regression and decision trees have deficiencies in non-linear data processing, index redundancy elimination, and real-time performance, resulting in large evaluation errors and weak adaptability, and cannot comprehensively evaluate and optimize financial risks. Summary of the Invention

[0004] The purpose of the present invention is to propose a financial risk evaluation system for a tourism company to solve the problem of large evaluation errors in existing financial risk evaluations.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions: A financial risk evaluation system for a tourism company, including:

[0006] A data collection module that collects and integrates multi-source internal and external data to support dynamic risk assessment and early warning;

[0007] A hybrid algorithm evaluation module that integrates multiple models to dynamically evaluate risk levels and quantify the probability of financial risks;

[0008] A dynamic early warning response module that real-time identifies risk signals, triggers multi-level early warnings, and visualizes the conduction path;

[0009] An intelligent decision-making support module that matches a risk disposal solution library and provides corresponding decisions;

[0010] A self-learning optimization module that iteratively optimizes evaluation parameters through cases.

[0011] Further, the data collection module needs to collect the following multi-dimensional data to establish a perfect data system:

[0012] Collect core financial data, identify the balance sheet, cash flow statement and profit information statement, extract and integrate them into financial statements, identify bank statements and electronic payment records, and integrate them into fund flow data, identify purchase invoices and expense reimbursement forms, and extract and integrate cost accounting data;

[0013] Collect business operation data. For customers, collect data such as order conversion rate, customer retention rate, and advance payment turnover rate. For supply chain data, identify and collect data such as supplier payment terms and logistics costs. For tourism industry-specific data, identify and collect data such as scenic area carrying capacity and cancellation rate information;

[0014] Collect external environment data, integrate macroeconomic indicator data based on the growth rate of gross domestic product and exchange rate fluctuations, collect tourism attraction safety rating information from official Internet websites, and obtain the industry risk index.

[0015] Furthermore, the hybrid algorithm evaluation module realizes the intelligent conversion from data to evaluation. This system integrates traditional statistical models, machine learning algorithms and dynamic optimization mechanisms to form a progressive evaluation structure. The hybrid algorithm evaluation module uses multi-modal feature fusion technology to develop a heterogeneous data alignment engine to solve three types of data integration problems:

[0016] For structured financial statement data, use the improved Z-score standardization method to eliminate the dimension difference. The formula is as follows:

[0017]

[0018] Where X′ is the standardized data value, which is the result after being processed by the Z-score standardization method, eliminating the dimension difference and facilitating the comparison of data with different dimensions on the same scale; X is the original data value; α is the industry adjustment coefficient; from the industry benchmark database; σ is the standard deviation of the data; μ is the average value of the data;

[0019] For semi-structured data of contract texts, apply the BiLSTM-CRF model to extract key clause elements, and use the bidirectional long short-term memory network and conditional random field model to extract key clause elements such as payment cycle and liquidated damages clause;

[0020] For unstructured data, construct an emotion dictionary transfer learning model to calculate the industry risk index. The formula is as follows:

[0021]

[0022] Where S m represents the industry analysis index; w i is the weight, which is dynamically determined by web crawler frequency analysis; Polarity(s i)$\widetilde{s}_{i}$ is the sentiment polarity of the $i$-th public opinion information calculated by the sentiment dictionary transfer learning model. Through this formula, feature extraction and fusion of different types of multi-modal data can be effectively carried out, thus solving the problem of data integration.

[0023] Furthermore, the dynamic warning response module uses an improved fuzzy dynamic programming algorithm to construct a dynamic threshold model in combination with the characteristics of financial data streams, and quantifies the risk level through a fuzzy membership function to solve the defect that traditional Boolean logic cannot handle boundary fuzziness. The formula is:

[0024]

[0025] where: $x$ is the input variable, representing the specific value or state for which the membership degree is to be evaluated. In the dynamic threshold model of financial data stream characteristics, $x$ can be the value of a certain financial indicator, such as profit, revenue, risk value, etc.;

[0026] $\mu$ Y $(x)$ is the membership degree of element $x$ in the fuzzy set $Y$, which represents the degree to which element $x$ belongs to the fuzzy set $Y$, and its value range is between $[0, 1]$. When $\mu$ Y $(x)=1$, it means that $x$ completely belongs to set $Y$; when $\mu$ Y $(x)=0$, it means that $x$ completely does not belong to set $Y$; values between $0$ and $1$ indicate that $x$ partially belongs to set $Y$;

[0027] $c$ is the center point of the membership function, representing the central position of the fuzzy set $Y$. In financial risk assessment, $c$ can represent a benchmark risk value or expected value. When $x = c$, $\mu$ Y $(x)=1$, indicating that $x$ completely conforms to the definition of the fuzzy set $Y$;

[0028] $a$ is the width parameter of the membership function, controlling the width or fuzziness of the membership function. A larger value of $a$ will make the membership function wider, indicating less sensitivity to changes in $x$, and a smaller value of $a$ will make the membership function narrower, indicating more sensitivity to changes in $x$. In financial risk assessment, $a$ can represent the risk tolerance or uncertainty range;

[0029] $b$ is the shape parameter of the membership function, controlling the shape of the membership function. A larger value of $b$ will make the shape of the membership function sharper, indicating more sensitivity to changes in $x$ close to $c$, and a smaller value of $b$ will make the shape of the membership function flatter, indicating less sensitivity to changes in $x$ close to $c$. In financial risk assessment, $b$ can represent the risk sensitivity or risk preference;

[0030] The parameters $a$, $b$, and $c$ are self-learned and calibrated through historical risk event data, adjusting the $c$, $a$, and $b$ parameters to adapt to different risk assessment requirements, solving the defect that traditional Boolean logic cannot handle boundary fuzziness, and accurately reflecting the uncertainty of financial data;

[0031] The dynamic early warning response module uses an integrated LSTM-Attention network to capture abnormal fluctuation patterns of capital turnover rate and accounts receivable cycle time series indicators to predict quarterly capital gaps. The dynamic early warning response module uses a graph convolutional network to analyze risk propagation paths, identify key nodes, and realize visualization from a single red and yellow light warning to risk transmission paths.

[0032] Furthermore, the intelligent decision support module generates a Pareto optimal solution set based on risk levels and company strategic goals, and previews the impact of decisions, builds a component strategy knowledge base, and establishes an intelligent matching system containing 5,000+ historical disposal cases. It uses a deep collaborative filtering algorithm to calculate the matching degree between the current risk scenario and the case library through cosine similarity;

[0033] Develop a multi-task learning model to simultaneously predict the economic value added of different schemes such as equity financing and asset securitization, combine the economic value added model to simulate the economic value added of the schemes, and provide a preview of the financial impact after the decision.

[0034] Furthermore, the self-learning optimization module establishes a risk disposal case library, optimizes algorithm parameters through reinforcement learning, and realizes dynamic calibration of warning thresholds.

[0035] The parameter optimization layer uses a two-way learning algorithm. Through two-way data flow and model interaction, the computer can learn from human feedback and pass its own knowledge to humans, thus achieving interactive learning between humans and computers and making reverse corrections through historical decision effect data.

[0036] Establish a knowledge iteration mechanism, build a risk event knowledge graph, automatically update association rules through entity relationship extraction technology, and identify new financial fraud patterns;

[0037] Build a digital twin sandbox, use Monte Carlo simulation to preview the financial impact of the decision, and simulate the changes in customer churn caused by austerity policies. The simulation process is as follows:

[0038] Year 1: Generate churn rate r1 and calculate the number of remaining customers N1 = N0 × (1-r1);

[0039] Year 2: Assuming the churn rate remains at r1, update the number of customers N2 = N1 × (1-r1) year by year; ......

[0041] Year t; assuming the churn rate remains at r t-1 , update the number of customers N year by year t =N t-1 ×(1-r t-1 );

[0042] Calculate profit: The annual profit is Profit t = N t ×(P - C) - H;

[0043] Wherein, the initial passenger flow is N0, the churn rate is r, the unit price per customer is P, the cost per customer is C, the fixed cost is H, and N t represents the number of customers in the t-th year; Monte Carlo simulation decomposes financial indicators into functions of random variables, generates a large number of scenarios in combination with probability distributions, and finally outputs the statistical distribution of the results, simulates the austerity policy case, and the simulation results show the impact of the customer churn rate. Enterprises need to weigh cost savings against the long-term impact of customer churn, or hedge risks through additional strategies such as customer retention incentives.

[0044] The beneficial effects brought by the technical solution provided by the present invention at least include:

[0045] The present invention builds a digital twin sandbox, pre-acts the financial impact of decisions through Monte Carlo simulation, simulates the possible changes in the customer churn rate caused by austerity policies, and based on the simulation results shows the possible impacts caused by changes in the customer churn rate, facilitating the tourism company to weigh costs against the long-term impact of customer churn and make corresponding strategic adjustments, and hedging risks through additional strategies such as customer retention incentives.

[0046] The present invention collects multi-dimensional data from a wide range of sources, establishes a perfect data system covering structured and unstructured data sources. After information collection, the system integrates traditional statistical models, machine learning algorithms and dynamic optimization mechanisms to form a progressive evaluation structure, develops a heterogeneous data alignment engine for data extraction and standardization, and solves the problem of integrating multiple types of data.

[0047] The present invention constructs a dynamic threshold model by using an improved fuzzy dynamic programming algorithm in combination with the characteristics of financial data streams, and quantifies the risk level through fuzzy membership functions, solving the defect that traditional Boolean logic cannot handle boundary fuzziness.

[0048] The two-way learning algorithm enables the computer to learn from human feedback while also transmitting its own knowledge to humans through shared data and fusion models. Based on the feedback loop, the computer can continuously adjust its model parameters to more quickly adapt to complex tasks; two-way learning improves the information transmission speed of traditional financial analysis and evaluation. Brief Description of the Drawings

[0049] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0050] Figure 1 Schematic diagram of the system provided by the embodiment of the present invention. Detailed implementation manners

[0051] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a financial risk evaluation system for a tourism company proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0053] The following embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention.

[0054] The following specifically describes the specific solution of a financial risk evaluation system for a tourism company provided by the present invention in conjunction with the accompanying drawings.

[0055] Embodiment

[0056] Please refer to Figure 1 , which shows a schematic diagram of the system composition of a financial risk evaluation system for a tourism company provided by an embodiment of the present invention. The system includes:

[0057] A data collection module that collects and integrates multi-source internal and external data to support dynamic risk assessment and early warning;

[0058] A hybrid algorithm evaluation module that integrates multiple models to dynamically evaluate the risk level and quantify the probability of financial risk;

[0059] A dynamic early warning response module that real-time identifies risk signals, triggers multi-level early warnings, and visualizes the conduction path;

[0060] An intelligent decision-making support module that matches the risk disposal plan library and simulates the financial impact of strategies;

[0061] A self-learning optimization module that iteratively optimizes the evaluation parameters and early warning thresholds through cases.

[0062] The data acquisition module needs to collect multi-dimensional data, establish a complete data system that covers structured and unstructured data sources, including the following data:

[0063] Collect core financial data, identify the balance sheet, cash flow statement, and profit information statement, and extract and integrate them into financial statements; identify bank statements and electronic payment records, and extract and calculate the fund flow data; identify purchase invoices and expense reimbursement forms, and extract and calculate the cost accounting data.

[0064] Collect business operation data. On the customer side, collect order conversion rate, customer retention rate, and prepayment turnover rate data to evaluate the financial risk impact on the customer side; identify supplier payment periods and logistics costs to evaluate the financial analysis of supply chain data; collect specific scenic area carrying capacity and cancellation rate information to analyze and evaluate the characteristic data of the tourism industry.

[0065] Collect external environment data. Based on the growth rate of the gross domestic product and exchange rate fluctuations, integrate macroeconomic indicator data, collect information on the safety ratings of tourist attractions from official Internet websites to obtain the industry risk index; obtain relevant public opinion data from evaluations on multiple OTA platforms and social media dissemination.

[0066] Data acquisition involves multiple data formats, covering structured data, semi-structured data, and unstructured data. For structured data such as data tables and XML files, SQL queries and ETL tools are used; semi-structured data such as logs and spreadsheets are processed using regular expression parsing technology; unstructured data such as PDF contracts, scanned bills, and voice records are extracted using OCR and NLP technologies; cross-system data is connected to external data sources such as the tax system and credit information platform through APIs to achieve real-time retrieval of data such as industrial and commercial information and administrative penalties.

[0067] Standardize data specifications, develop an intelligent data cleaning engine, use K-means clustering to identify outliers, align time series data using dynamic time warping, and build an industry-specific data dictionary to achieve semantic unity.

[0068] Among them, the hybrid algorithm evaluation module:

[0069] Establish a three-layer and four-dimensional system. The three layers refer to the data layer, algorithm layer, and decision-making layer; the four dimensions refer to the financial dimension, business dimension, environmental dimension, and governance dimension, to achieve intelligent conversion from data to evaluation. This system integrates traditional statistical models, machine learning algorithms, and dynamic optimization mechanisms to form a progressive evaluation structure and a multi-modal feature fusion technology, and develops a heterogeneous data alignment engine to solve three types of data integration problems:

[0070] For structured financial statement data, use the improved Z-score standardization method to eliminate the dimension difference. The formula is as follows:

[0071]

[0072] Where X' is the standardized data value, which is the result of being processed by the Z-score standardization method, eliminating the dimension difference and facilitating the comparison of data with different dimensions on the same scale; X is the original data value; α is the industry adjustment coefficient, coming from the industry benchmark database; σ is the standard deviation of the data; μ is the average value of the data.

[0073] This method uses the Z-score standardization method to standardize the data, and the formula is The purpose of this step is to eliminate the dimension difference, enabling the comparison and analysis of data with different dimensions on the same scale. The standardized data is then multiplied by the industry adjustment coefficient α to adapt to the characteristics of different industries. The industry adjustment coefficient α comes from the industry benchmark database and reflects the specific characteristics and differences of different industries.

[0074] For semi-structured data in contract texts, the BiLSTM-CRF model is applied to extract key clause elements. The bidirectional long short-term memory network (BiLSTM) and conditional random field (CRF) models are used to extract key clause elements, such as the payment cycle and liquidated damages clause. The bidirectional long short-term memory network can capture the context information of the text, helping to understand the complex relationships and semantics in the contract text. The conditional random field model is used for sequence labeling tasks and can effectively identify and extract the key clauses and elements in the contract.

[0075] Unstructured data: Construct an emotion dictionary transfer learning model to calculate the industry risk index, and the formula is as follows:

[0076]

[0077] Where S t represents the industry analysis index; w i is the weight, dynamically determined through web crawler frequency analysis; Polarity(s i ) is the sentiment polarity of the i-th public opinion information calculated by the emotion dictionary transfer learning model.

[0078] Construct an emotion dictionary transfer learning model to calculate the sentiment polarity Polarity(s i ) of each public opinion information. The weight w i is dynamically determined through web crawler frequency analysis, reflecting the importance and influence of different public opinion information. Then, multiply the sentiment polarity Polarity(s i ) of each public opinion information by the corresponding weight w i and sum up all the public opinion information to obtain the industry risk index S t, through this formula, feature extraction and fusion of different types of multimodal data can be effectively carried out, thus solving the problem of data integration.

[0079] Among them, the dynamic warning response mode:

[0080] Using the improved fuzzy dynamic programming (FDP) algorithm, combined with the characteristics of financial data flow to construct a dynamic threshold model, quantifying the risk level through the fuzzy membership function, to solve the defect that traditional Boolean logic cannot handle the boundary fuzziness. The formula is:

[0081]

[0082] Where: x is the input variable, representing the specific value or state to evaluate its membership degree. In the dynamic threshold model of financial data flow characteristics, x can be the value of a certain financial indicator, such as profit, revenue, risk value, etc.;

[0083] μ Y (x) is the membership degree of element x in the fuzzy set Y, which represents the degree to which element x belongs to the fuzzy set Y, and its value range is between [0, 1]. When μ Y (x) = 1, it means that x completely belongs to the set Y; when μ Y (x) = 0, it means that x completely does not belong to the set Y; values between 0 and 1 indicate that x partially belongs to the set Y;

[0084] c is the center point of the membership function, representing the central position of the fuzzy set Y. In financial risk assessment, c can represent a benchmark risk value or expected value. When x = c, μ Y (x) = 1, indicating that x completely conforms to the definition of the fuzzy set Y;

[0085] a is the width parameter of the membership function, controlling the width or fuzziness of the membership function. A larger value of a will make the membership function wider, indicating less sensitivity to changes in x, and a smaller value of a will make the membership function narrower, indicating more sensitivity to changes in x. In financial risk assessment, a can represent the risk tolerance or uncertainty range;

[0086] b is the shape parameter of the membership function, controlling the shape of the membership function. A larger value of b will make the shape of the membership function sharper, indicating more sensitivity to changes in x close to c, and a smaller value of b will make the shape of the membership function flatter, indicating less sensitivity to changes in x close to c. In financial risk assessment, b can represent the risk sensitivity or risk preference;

[0087] The parameters a, b, and c are self - learned and calibrated through historical risk event data, adjusting the c, a, and b parameters to adapt to different risk assessment requirements, solving the defect that traditional Boolean logic cannot handle the boundary fuzziness, and accurately reflecting the uncertainty of financial data;

[0088] Using an integrated LSTM-Attention network to capture the abnormal fluctuation patterns of time series indicators such as the capital turnover rate and the accounts receivable cycle, for quarterly capital gap prediction. The dynamic early warning response module integrates the output of an improved F-score model, develops a three-dimensional risk heat map based on the macroeconomic sensitivity coefficient of the supply chain risk index using association rules, and uses a convolutional network to analyze the risk propagation path, identify key nodes, and visualize risk conduction.

[0089] For example, when the system detects that the quick ratio of a subsidiary fluctuates continuously by 3 standard deviations, it automatically triggers:

[0090] First-level response: Freeze the approval authority for high-risk business;

[0091] Second-level response: Start the stress test model to simulate the probability of cash flow break;

[0092] Third-level response: Generate a risk disposal plan and push it to the decision-making module.

[0093] Among them, the intelligent decision support module:

[0094] Based on the integration of risk levels and corporate strategic goals, generate a Pareto optimal solution set, preview the decision impact, construct a policy knowledge base, establish an intelligent matching system containing more than 5,000 historical disposal cases, and use a deep collaborative filtering algorithm to calculate the matching degree between the current risk scenario and the case library through cosine similarity:

[0095]

[0096] In the formula, sim(A,B) represents the similarity between vector A and vector B, and its value ranges from -1 to 1. The closer the value is to 1, the more similar the two vectors are; the closer the value is to -1, the less similar the two vectors are; and a value of 0 indicates that the two vectors are orthogonal, meaning they are completely unrelated in direction; n represents the dimension of the vector. The Σ in the formula represents summation, and i = 1 to n means operating on each element of vector A and vector B. A i and B i respectively represent the i-th element of vector A and vector B; the numerator part represents the dot product of vector A and vector B, that is, the sum of the products of the corresponding elements of the two vectors; the denominator part is the product of the magnitudes of vector A and vector B, where represents the magnitude of vector A, represents the magnitude of vector B, and the magnitude of a vector is the square root of the sum of the squares of the vector elements.

[0097] Develop a multi-task learning model to predict the Economic Value Added (EVA) of different scenarios such as equity financing and asset securitization simultaneously. Combine the EVA model to simulate the EVA of the scenarios and provide a preview of the financial impact after the decision.

[0098] Self-learning optimization module:

[0099] Establish a risk disposal case library, optimize the algorithm parameters through reinforcement learning, and achieve dynamic calibration of the warning threshold.

[0100] Parameter optimization layer, adopting a two-way learning algorithm. The two-way learning algorithm is a technology that allows computers and humans to educate each other in intelligent learning. Through two-way data flow and model interaction, the computer can learn from human feedback and at the same time transmit its own knowledge to humans. The core of this learning method lies in data sharing, model fusion, and feedback mechanism, so as to achieve interactive learning between humans and computers and reverse correction through historical decision-making effect data.

[0101] Establish a knowledge iteration mechanism, construct a knowledge graph of risk events, automatically update the association rules through entity relationship extraction technology, and identify current new financial fraud models.

[0102] Dynamic optimization. The NSGA-II algorithm is a multi-objective evolutionary optimization algorithm based on non-dominated sorting. Its core idea is to generate a diverse solution set through non-dominated sorting, elitist strategy, and crowding degree calculation, and gradually approach the Pareto front in the iterative process. On this basis, the generation method of the crossover operator coefficient is optimized to improve the efficiency of the crossover operation and the diversity of solutions; the crowding degree calculation method is optimized to make it more accurate and efficient.

[0103] Construct a digital twin sandbox, preview the financial impact of decisions through Monte Carlo simulation, and simulate the change in the customer churn rate that may be caused by tightening policies. The simulation process is as follows:

[0104] Related parameters: The initial passenger flow is N0, the churn rate is r, the unit price per customer is P, the cost per customer is C, the fixed cost is H, and N t represents the number of customers in the t-th year.

[0105] Year 1: Generate the churn rate r1, and calculate the remaining number of customers N1 = N0×(1 - r1);

[0106] Year 2: Assume the churn rate remains r1, and update the number of customers year by year N2 = N1×(1 - r1); ......

[0108] Year t; Assume the churn rate remains r t-1 , and update the number of customers year by year N t = N t-1×(1 - r t-1 );

[0109] Calculate the profit: The annual profit Profit t = N t ×(P - C) - H.

[0110] The Monte Carlo simulation decomposes financial metrics into functions of random variables, combines probability distributions to generate a large number of scenarios, and finally outputs the statistical distribution of the results, simulating the austerity policy case and showing the possible impacts caused by changes in the customer churn rate according to the simulation results; enterprises need to weigh the costs against the long-term impacts of customer churn.

[0111] In this way, a financial risk assessment system for a tourism company can be realized.

[0112] The above-described embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A financial risk assessment system for a tourism company, characterized in that, Including: A data collection module that collects and integrates multi-source internal and external data to support dynamic risk assessment and early warning. A hybrid algorithm evaluation module that fuses multiple models to dynamically evaluate risk levels and quantify the probability of financial risks. A dynamic early warning response module that real-time identifies risk signals, triggers multi-level early warnings, and visualizes the conduction path. An intelligent decision-making support module that matches the risk disposal solution library and provides corresponding decisions. A self-learning optimization module that iteratively optimizes evaluation parameters through cases.

2. A financial risk evaluation system for a tourism company according to claim 1, characterized in that: The data collection module needs to collect the following multi-dimensional data to establish a complete data system: Collect core financial data, identify the balance sheet, cash flow statement, and profit information statement, and extract and integrate the financial statements. Identify bank statements and electronic payment records and integrate the fund flow data. Identify purchase invoices and expense reimbursement forms and extract and integrate the cost accounting data. Collect business operation data. For customers, collect data such as order conversion rate, customer retention rate, and prepayment turnover rate. For supply chain data, identify and collect data such as supplier payment terms and logistics costs. For tourism industry-specific data, identify and collect data such as scenic spot carrying capacity and cancellation rate information. Collect external environment data. Integrate macroeconomic indicator data based on the growth rate of gross domestic product and exchange rate fluctuations. Collect tourism scenic spot safety rating information from official Internet websites to obtain the industry risk index.

3. A financial risk evaluation system for a tourism company according to claim 1, characterized in that: The hybrid algorithm evaluation module realizes the intelligent conversion from data to evaluation. This system integrates traditional statistical models, machine learning algorithms, and dynamic optimization mechanisms to form a progressive evaluation structure. The hybrid algorithm evaluation module uses multi-modal feature fusion technology to develop a heterogeneous data alignment engine to solve three types of data integration problems: For structured financial statement data, an improved Z-score standardization method is adopted to eliminate the dimension difference. The formula is as follows: Where X′ is the standardized data value, which is the result after being processed by the Z-score standardization method, eliminating the dimension difference and facilitating the comparison of data with different dimensions on the same scale; X is the original data value; α is the industry adjustment coefficient, from the industry benchmark database; σ is the standard deviation of the data; μ is the average value of the data. For semi-structured data in contract texts, the BiLSTM-CRF model is applied to extract key clause elements, and the bidirectional long short-term memory network and conditional random field model are used to extract key clause elements such as payment cycle and liquidated damages clause. For unstructured data, a sentiment dictionary transfer learning model is constructed to calculate the industry risk index. The formula is as follows: Among which S m represents the industry analysis index; w i is the weight, dynamically determined by analyzing the frequency of web crawlers; Polarity(s i ) is the sentiment polarity of the i-th piece of public opinion information calculated by the sentiment dictionary transfer learning model. Through this formula, feature extraction and fusion of different types of multimodal data can be effectively carried out, thus solving the problem of data integration.

4. A financial risk evaluation system for a tourism company according to claim 1, characterized in that: The dynamic early warning response module uses an improved fuzzy dynamic programming algorithm, combines the characteristics of financial data flow to construct a dynamic threshold model, and quantifies the risk level through a fuzzy membership function to solve the defect that traditional boolean logic cannot handle the boundary fuzziness. The formula is: Where: x is an input variable, representing a specific value or state for which the membership degree is to be evaluated. In the dynamic threshold model of financial data stream characteristics, x can be the value of a certain financial indicator, such as profit, revenue, risk value, etc.; μ Y (x) is the membership degree of the element x in the fuzzy set Y, which represents the degree to which the element x belongs to the fuzzy set Y, and its value range is between [0, 1]. When μ Y (x) = 1, it means that x completely belongs to the set Y; when μ Y (x) = 0, it means that x completely does not belong to the set Y; the value between 0 and 1 means that x partially belongs to the set Y; c is the center point of the membership function, representing the central position of the fuzzy set Y. In financial risk assessment, c can represent a benchmark risk value or expected value. When x = c, μ Y (x) = 1, indicating that x completely conforms to the definition of the fuzzy set Y; a is the width parameter of the membership function, controlling the width or fuzziness of the membership function. A larger value of a will make the membership function wider, indicating less sensitivity to changes in x, and a smaller value of a will make the membership function narrower, indicating more sensitivity to changes in x. In financial risk assessment, a can represent the risk tolerance or uncertainty range; b is the shape parameter of the membership function, controlling the shape of the membership function. A larger value of b will make the shape of the membership function sharper, indicating more sensitivity to changes in x close to c, and a smaller value of b will make the shape of the membership function flatter, indicating less sensitivity to changes in x close to c. In financial risk assessment, b can represent the risk sensitivity or risk preference; The parameters a, b, and c are self-learned and calibrated through historical risk event data, adjusting the c, a, and b parameters to adapt to different risk assessment requirements, solving the defect that traditional boolean logic cannot handle boundary fuzziness, and accurately reflecting the uncertainty of financial data; The dynamic early warning response module uses an integrated LSTM-Attention network to capture abnormal fluctuation patterns of time series indicators such as the capital turnover rate and accounts receivable cycle, and conducts quarterly capital gap prediction. The dynamic early warning response module uses a graph convolutional network to analyze the risk propagation path, identify key nodes, and realize the visualization of the risk conduction path from a single red-yellow light warning; 5. A financial risk assessment system for a tourism company as described in claim 1, wherein: The intelligent decision support module generates a Pareto optimal solution set based on the risk level and the company's strategic goals, previews the decision impact, constructs a strategy knowledge base, establishes an intelligent matching system containing more than 5,000 historical disposal cases, and uses a deep collaborative filtering algorithm to calculate the matching degree between the current risk scenario and the case base through cosine similarity; Develop a multi-task learning model to simultaneously predict the economic value added of different solutions such as equity financing and asset securitization, and simulate the economic value added of the solutions in combination with the economic value added model to provide a preview of the financial impact after the decision; 6. A financial risk assessment system for a tourism company as described in claim 1, wherein: The self-learning optimization module establishes a risk disposal case base and dynamically calibrates the early warning threshold through a reinforcement learning optimization algorithm; The parameter optimization layer uses a two-way learning algorithm. The two-way learning algorithm enables the computer to learn from human feedback through two-way data flow and model interaction, and at the same time can transmit its own knowledge to humans, realizing interactive learning between humans and computers, and reverse correcting through historical decision effect data; Establish a knowledge iteration mechanism, construct a risk event knowledge graph, automatically update association rules through entity relationship extraction technology, and identify current new financial fraud models; Construct a digital twin sandbox, preview the financial impact of the decision through Monte Carlo simulation, simulate the change in the customer churn rate caused by the tightening policy, and the simulation process is as follows: Year 1: Generate the churn rate r1, and calculate the remaining number of customers N1 = N0 × (1 - r1); Year 2: Assume the churn rate remains r1, and update the number of customers year by year N2 = N1 × (1 - r1); ...... Year t; assuming the churn rate remains at r t-1 , update the number of customers N year by year t = N t-1 × (1 - r t-1 ); Calculate profit: Annual profit Profit t = N t × (P - C) - H; Among them, the initial passenger flow is N0, the churn rate is r, the average customer spending is P, the cost per customer is C, the fixed cost is H, and N t represents the number of customers in the t-th year; Monte Carlo simulation decomposes financial indicators into functions of random variables, generates a large number of scenarios in combination with probability distributions, and finally outputs the statistical distribution of the results. Simulate the austerity policy case, and the simulation results show the impact of the customer churn rate. Enterprises need to weigh cost savings against the long-term impact of customer churn, or hedge risks through additional strategies such as customer retention incentives.

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