Medical beauty financial data analysis method based on big data

Abstract

The invention belongs to the field of financial data analysis, and particularly relates to a medical beauty financial data analysis method based on big data, and the method comprises the steps: financial relation graph construction, filtering of random noise of financial data, graph structure feature extraction, abnormal high-risk node recognition, financial condition evaluation and privacy security response. According to the scheme, the graph structure characteristics of the financial data are utilized, random noise in the financial data is filtered out, the importance of nodes in a fund link is quantified, a transaction detection link is formed, breadth-first search and business logic verification are combined, high-risk nodes are positioned, and hidden risks are identified; through fuzzy labeling and a rule base, financial indexes are mapped into a fuzzy set, model response is tested through a trap problem, sensitive information is labeled, a privacy vulnerability thermodynamic diagram is generated, a vulnerability source is traced by adopting hardware acceleration and an attention mechanism, hierarchical privacy reinforcement is implemented, the sensitive information leakage risk is reduced, and the privacy security is improved. And the requirements of tax administration and privacy regulations are met.

Description

Technical Field

[0001] This invention relates to the field of financial data analysis, specifically to a big data-based method for analyzing financial data in the medical aesthetics industry. Background Technology

[0002] In recent years, the domestic medical aesthetics market has continued to expand. Big data-based financial data analysis methods for medical aesthetics refer to a method that utilizes big data technology to support the analysis of financial data in the medical aesthetics industry.

[0003] Among existing similar solutions, such as CN118898524B, a financial settlement management method and system based on waste recycling data analysis, this solution addresses the technical problems of using a single calculation strategy, which cannot accurately determine the settlement amount, cannot generate differentiated settlement strategies based on data analysis results, and cannot guarantee the accuracy of settlement results. Firstly, it assesses the difficulty of waste identification and processing, analyzes waste images, calculates the deviation between the waste recycling volume and the actual recycling volume, outputs anomaly alerts, and re-uploads the images. In the settlement processing stage, it adjusts the preset settlement unit price based on the degree of soiling, and combines the waste recycling volume and the adjusted unit price to generate the final financial settlement result. This technical approach solves the problem of low accuracy in traditional waste recycling settlement due to soiling or data anomalies. However, existing technologies cannot be directly transplanted to address tax and financial issues in the medical aesthetics industry, such as hidden income and high commissions without tax payment, and lack structured correlation methods.

[0004] Furthermore, for example, CN116542801B describes a financial data analysis method and system. This solution addresses the technical problem of lacking research on corporate decision-making behavior analysis based on financial data. First, it selects a preset execution model corresponding to the enterprise type from a model library, extracts financial data features, and generates expected financial feature data. At the same time, it compares the expected financial feature data with the current financial feature data to find the combination with the smallest difference. The corresponding behavioral data is the target year's behavioral data. Finally, it introduces economic environment data to correct the financial feature data, thus improving the accuracy of inference. However, existing financial analysis models may leak sensitive information and lack effective privacy protection and compliance verification mechanisms. Summary of the Invention

[0005] To address the aforementioned issues and overcome the shortcomings of existing technologies, this invention provides a big data-based financial data analysis method for the medical aesthetics industry. Addressing the limitations of existing technologies in directly addressing tax and financial issues such as hidden income and unpaid high commissions in the medical aesthetics industry, and the lack of structured correlation methods, this solution utilizes the graph structure characteristics of financial data to filter out random noise, quantify the importance of nodes in the funding chain, and establish transaction detection links by associating nodes with edges. Combining breadth-first search and business logic verification, high-risk nodes are located to identify hidden risks. Furthermore, addressing the potential leakage of sensitive information and the lack of effective privacy protection and compliance verification mechanisms in existing financial analysis models, this solution maps financial indicators to fuzzy sets through fuzzy labeling and rule bases. It tests model responses using trap questions, marks sensitive information, generates a privacy vulnerability heatmap, employs hardware acceleration and attention mechanisms to trace the source of vulnerabilities, and implements tiered privacy hardening, reducing the risk of sensitive information leakage and ensuring the model complies with tax and privacy regulations.

[0006] The technical solution adopted by this invention is as follows: This invention provides a medical aesthetics financial data analysis method based on big data, which includes the following steps:

[0007] Step S1: Financial Relationship Graph Construction. Specifically, collect financial data from medical aesthetic institutions, including revenue ledgers, expenditure vouchers, channel agreements, and stored-value card records. Map the financial data to nodes and edges. The nodes include medical aesthetic institution nodes, customer nodes, and channel partners. The edges include payment collection, commission rebates, refunds, cost expenditures, and tax declarations. Construct a financial relationship graph.

[0008] Step S2: Filter out random noise from the financial data using the following formula: ;

[0009] In the formula, express Financial data after constant noise filtering express The original financial data at all times, Represents the smoothing coefficient. express Financial data after constant noise filtering express Differences between adjacent financial data reflecting trend changes at any given moment;

[0010] Step S3: Graph structure feature extraction, specifically, extracting numerical and temporal features from the financial data. The numerical features include revenue amount, expenditure amount, channel commission rate, and zero-balance balance of stored-value cards. The temporal features include transaction time interval, stored-value card validity period, and rebate cycle. Calculate the in-edge density of the nodes in the financial relationship graph using the following formula: ;

[0011] In the formula, Represents a node Entered edge density, This represents the damping factor used to simulate the probability of random jumps at nodes, preventing dead ends and stagnation in the financial relationship diagram. Represents a node All incoming edge nodes gather, Represents a node The number of outgoing edges, Represents a node The density of incoming edges;

[0012] Step S4: Identify high-risk, abnormal nodes;

[0013] Step S5: Financial Status Assessment;

[0014] Step S6: Privacy and security response, used for compliance verification, to reduce the risk of privacy leakage in financial analysis models.

[0015] Furthermore, in step S4, identifying anomalous high-risk nodes specifically includes the following steps:

[0016] Step S41: Identify abnormal clustering patterns through breadth-first search;

[0017] Step S42: High-risk detection, specifically, identifying high-risk nodes by combining the numerical features, the temporal features, and the node incoming edge density;

[0018] Step S43: Verify the associated business logic, specifically by combining the anomaly aggregation pattern with the characteristics of the medical aesthetics industry to verify anomalies and locate high-risk nodes.

[0019] Furthermore, in step S5, the financial condition assessment specifically includes the following steps:

[0020] Step S51: Fuzzy labeling. Specifically, collect common financial indicators that characterize the company's financial status, and based on the duration, intensity, and direction of change, pre-define semantic labels representing the trend characteristics of the financial data. Then, fuzzify and map the numerical trend of the financial data into a fuzzy set. The formula used is as follows: ;

[0021] In the formula, For the fuzzy set after fuzzification, Describes a membership function;

[0022] Step S52: Develop a rule base to ensure that multiple financial indicators are obtained in the order of dependency when they are related to each other, and avoid calculation failure due to missing basic indicators. Specifically, combine fuzzy sets to define assessment rules for evaluating the company's financial status, use logical reasoning tools to complete implicit indicators and check for logical contradictions, consider the transitive dependency relationship between financial indicators and implicit indicators, and transform the execution order of assessment rules into a graph structure to obtain a dependency graph. If any dependency of a financial indicator is missing, the calculation process is suspended.

[0023] A financial analysis model is constructed based on financial indicators and implicit indicators.

[0024] Furthermore, in step S6, the privacy and security response specifically includes the following steps:

[0025] Step S61: In-depth privacy risk detection for adversarial testing. Specifically, a trap question is simulated using a natural language template. The trap question is input into the financial analysis model, and all responses of the financial analysis model are captured. The responses are scanned using regular expressions, and sensitive information is marked once it is found.

[0026] The leaked sensitive information is converted into a privacy vulnerability heatmap according to type and frequency, and the privacy vulnerability heatmap is used as a compliance score.

[0027] Step S62: Deploy a lightweight financial analysis model to address the computing power bottleneck in real-time monitoring of the financial analysis model. Specifically, use an FPGA chip to compile the trap problem into an instruction stream, thereby accelerating the review of sensitive information.

[0028] Step S63: Optimize the questioning pattern to locate sensitive information in the response and trace the source of the vulnerability. Specifically, the response is split word by word, sensitive information is marked, and through the attention mechanism, the words in the trap questions that the financial analysis model will focus on when the response contains sensitive information are analyzed. These words are recorded as prompt words. The prompt words that trigger sensitive information are traced back to the prompt words, and the prompt words and the corresponding trap question context are associated to form a traceability chain from the prompt words to the response containing sensitive information, so as to find dangerous questioning patterns.

[0029] Step S64: Graded privacy hardening, specifically, classifying dangerous questioning patterns according to the severity of sensitive information leakage, implementing privacy hardening on the response of the financial analysis model, and achieving graded response;

[0030] Step S65: The privacy-enhanced financial analysis model outputs financial assessment results.

[0031] This invention provides a big data-based method for analyzing financial data in the medical aesthetics industry. The beneficial effects achieved by this invention using the above solution are as follows:

[0032] (1) In view of the fact that existing technologies cannot be directly transplanted to address the tax and financial issues of hidden income and high commissions without taxation in the medical aesthetics industry, and the lack of structured association methods, this solution utilizes the graph structure characteristics of financial data to filter out random noise in financial data, quantify the importance of nodes in the capital chain, associate nodes with edges to form a transaction detection chain, and combine breadth-first search and business logic verification to locate high-risk nodes and identify hidden risks.

[0033] (2) In response to the technical problems of existing financial analysis models potentially leaking sensitive information and lacking effective privacy protection and compliance verification mechanisms, this solution maps financial indicators to fuzzy sets through fuzzy labeling and rule base, tests the model response through trap questions, marks sensitive information and generates a privacy vulnerability heatmap, uses hardware acceleration and attention mechanism to trace the source of vulnerabilities, implements graded privacy hardening, reduces the risk of sensitive information leakage, and ensures that the model complies with tax and privacy regulations. Attached Figure Description

[0034] Figure 1 A flowchart illustrating a big data-based medical aesthetics financial data analysis method provided by this invention;

[0035] Figure 2 This is a schematic diagram of step S4.

[0036] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0037] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0038] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0039] Example 1, see Figures 1 to 2 This invention provides a big data-based method for analyzing financial data in the medical aesthetics industry, which includes the following steps:

[0040] Step S1: Financial Relationship Graph Construction. Specifically, collect financial data from medical aesthetic institutions, including revenue ledgers, expenditure vouchers, channel agreements, and stored-value card records. Map the financial data to nodes and edges. The nodes include medical aesthetic institution nodes, customer nodes, and channel partners. The edges include payment collection, commission rebates, refunds, cost expenditures, and tax declarations. Construct a financial relationship graph.

[0041] Step S2: Filter out random noise from the financial data using the following formula: ;

[0042] In the formula, express Financial data after constant noise filtering express The original financial data at all times, Represents the smoothing coefficient. express Financial data after constant noise filtering express Differences between adjacent financial data reflecting trend changes at any given moment;

[0043] Step S3: Graph structure feature extraction, specifically, extracting numerical and temporal features from the financial data. The numerical features include revenue amount, expenditure amount, channel commission rate, and zero-balance balance of stored-value cards. The temporal features include transaction time interval, stored-value card validity period, and rebate cycle. Calculate the in-edge density of the nodes in the financial relationship graph using the following formula: ;

[0044] In the formula, Represents a node Entered edge density, This represents the damping factor used to simulate the probability of random jumps at nodes, preventing dead ends and stagnation in the financial relationship diagram. Represents a node All incoming edge nodes gather, Represents a node The number of outgoing edges, Represents a node The density of incoming edges;

[0045] Step S4: Identify high-risk, abnormal nodes;

[0046] Step S5: Financial Status Assessment;

[0047] Step S6: Privacy and security response, used for compliance verification, to reduce the risk of privacy leakage in financial analysis models.

[0048] Example 2, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S4, the identification of abnormal high-risk nodes specifically includes the following steps:

[0049] Step S41: Identify abnormal clustering patterns through breadth-first search;

[0050] Step S42: High-risk detection, specifically, identifying high-risk nodes by combining the numerical features, the temporal features, and the node incoming edge density;

[0051] Step S43: Verify the associated business logic, specifically by combining the anomaly aggregation pattern with the characteristics of the medical aesthetics industry to verify anomalies and locate high-risk nodes.

[0052] Example 3, see Figures 1 to 2 This embodiment is based on the above embodiment. In step S5, the financial status assessment specifically includes the following steps:

[0053] Step S51: Fuzzy labeling. Specifically, collect common financial indicators that characterize the company's financial status, and based on the duration, intensity, and direction of change, pre-define semantic labels representing the trend characteristics of the financial data. Then, fuzzify and map the numerical trend of the financial data into a fuzzy set. The formula used is as follows: ;

[0054] In the formula, For the fuzzy set after fuzzification, Describes a membership function;

[0055] Step S52: Develop a rule base to ensure that multiple financial indicators are obtained in the order of dependency when they are related to each other, and avoid calculation failure due to missing basic indicators. Specifically, combine fuzzy sets to define assessment rules for evaluating the company's financial status, use logical reasoning tools to complete implicit indicators and check for logical contradictions, consider the transitive dependency relationship between financial indicators and implicit indicators, and transform the execution order of assessment rules into a graph structure to obtain a dependency graph. If any dependency of a financial indicator is missing, the calculation process is suspended.

[0056] A financial analysis model is constructed based on financial indicators and implicit indicators.

[0057] Example 4, see Figures 1 to 2This embodiment is based on the above embodiment. In step S6, the privacy and security response specifically includes the following steps:

[0058] Step S61: In-depth privacy risk detection for adversarial testing. Specifically, a trap question is simulated using a natural language template. The trap question is input into the financial analysis model, and all responses of the financial analysis model are captured. The responses are scanned using regular expressions, and sensitive information is marked once it is found.

[0059] The leaked sensitive information is converted into a privacy vulnerability heatmap according to type and frequency, and the privacy vulnerability heatmap is used as a compliance score.

[0060] Step S62: Deploy a lightweight financial analysis model to address the computing power bottleneck in real-time monitoring of the financial analysis model. Specifically, use an FPGA chip to compile the trap problem into an instruction stream, thereby accelerating the review of sensitive information.

[0061] Step S63: Optimize the questioning pattern to locate sensitive information in the response and trace the source of the vulnerability. Specifically, the response is split word by word, sensitive information is marked, and through the attention mechanism, the words in the trap questions that the financial analysis model will focus on when the response contains sensitive information are analyzed. These words are recorded as prompt words. The prompt words that trigger sensitive information are traced back to the prompt words, and the prompt words and the corresponding trap question context are associated to form a traceability chain from the prompt words to the response containing sensitive information, so as to find dangerous questioning patterns.

[0062] Step S64: Graded privacy hardening, specifically, classifying dangerous questioning patterns according to the severity of sensitive information leakage, implementing privacy hardening on the response of the financial analysis model, and achieving graded response;

[0063] Step S65: The privacy-enhanced financial analysis model outputs financial assessment results.

[0064] Example 5, see Figures 1 to 2 This embodiment is based on the above embodiment. In step S3, .

[0065] Example 6, see Figures 1 to 2 This embodiment is based on the above embodiment. In step S41, the abnormal aggregation mode specifically refers to the star-shaped subgraph, that is, multiple customer nodes are concentrated and point to a medical aesthetic institution node.

[0066] Example 7, see Figures 1 to 2This embodiment is based on the above embodiment. In step S42, the high-risk detection specifically refers to any node whose node incoming edge density ranks in the top 5% among all nodes, and whose recipient of the transaction corresponding to the numerical and temporal feature edges is a personal account, but receives transactions from customer nodes, then it is determined to be a high-risk node with non-institutional accounts concealing income.

[0067] Example 8, see Figures 1 to 2 This embodiment is based on the above embodiment. In step S43, the verification of the associated business logic specifically refers to the fact that medical aesthetic institutions rely on channel partners to acquire customers and often reduce revenue under the guise of sales refunds, but in reality, it is a rebate. Taking the abnormal clustering pattern associated nodes as the center, if the edge from the medical aesthetic institution node to the channel partner in the financial relationship graph is marked as a refund and the refund value exceeds 60% of the receipt value, and at the same time the channel partner node is not associated with the tax declaration edge, then it is determined that there is a high-risk node with rebate and non-tax payment behavior.

[0068] Example 9, see Figures 1 to 2 This embodiment is based on the above embodiment. In step S51, the semantic labels representing the trend characteristics of financial data are preset according to the duration, specifically "long-term", "medium-term" and "short-term".

[0069] Example 10, see Figures 1 to 2 This embodiment is based on the above embodiment. In step S51, the semantic labels representing the trend characteristics of financial data are preset according to the intensity of change, specifically "drastic", "moderate" and "slight".

[0070] Example 11, see Figures 1 to 2 This embodiment is based on the above embodiment. In step S51, the semantic labels that represent the trend characteristics of financial data are preset according to the direction, specifically "growth", "stable" and "decline".

[0071] Example 12, see Figures 1 to 2 This embodiment is based on the above embodiment. In step S52, the evaluation rule is specifically that a decline in the long-term current ratio leads to a poor financial condition.

[0072] Example 13, see Figures 1 to 2 This embodiment is based on the above embodiment. In step S52, the logical reasoning tool is specifically the Pellet inference engine.

[0073] Example 14, see Figures 1 to 2 This embodiment is based on the above embodiment. In step S64, the hierarchical response specifically includes:

[0074] Extremely high risk: Triggers fine-tuning of the financial analysis model, injecting differential noise;

[0075] High risk: Enable real-time desensitization and replace sensitive words;

[0076] Medium risk: Log and issue alert;

[0077] Low risk: Keep the default response.

[0078] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0079] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0080] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for analyzing medical aesthetic financial data based on big data, characterized in that: The method includes the following steps: Step S1: Financial Relationship Graph Construction. Specifically, this involves collecting financial data from medical aesthetic institutions, mapping the financial data to nodes and edges, and constructing a financial relationship graph. Step S2: Filter out random noise from the financial data; Step S3: Graph structure feature extraction, specifically, extracting numerical and temporal features from financial data, and calculating the in-edge density of nodes in the financial relationship graph; Step S4: Identify high-risk, abnormal nodes; Step S5: Financial Status Assessment; Step S6: Privacy and security response, used for compliance verification, to reduce the risk of privacy leakage in financial analysis models.

2. The medical aesthetics financial data analysis method based on big data according to claim 1, characterized in that: In step S4, identifying anomalous high-risk nodes specifically includes the following steps: Step S41: Identify abnormal clustering patterns through breadth-first search; Step S42: High-risk detection, specifically, identifying high-risk nodes by combining the numerical features, the temporal features, and the node incoming edge density; Step S43: Verify the associated business logic, specifically by combining the anomaly aggregation pattern with the characteristics of the medical aesthetics industry to verify anomalies and locate high-risk nodes.

3. The medical aesthetics financial data analysis method based on big data according to claim 2, characterized in that: In step S5, the financial condition assessment specifically includes the following steps: Step S51: Fuzzy labeling, specifically, collecting common financial indicators that characterize the company's financial status, and based on the duration, intensity and direction of change, pre-setting semantic labels that represent the trend characteristics of financial data, and fuzzifying the numerical trend of financial data into a fuzzy set. Step S52: Compile a rule base. Specifically, combine fuzzy sets to define assessment rules for evaluating the company's financial condition, use logical reasoning tools to complete implicit indicators, check for logical contradictions, consider the transitive dependencies between financial indicators and implicit indicators, transform the execution order of assessment rules into a graph structure, obtain a dependency graph, and if any financial indicator's dependency is missing, the calculation process is paused. A financial analysis model is constructed based on financial indicators and implicit indicators.

4. The medical aesthetics financial data analysis method based on big data according to claim 3, characterized in that: In step S6, the privacy and security response specifically includes the following steps: Step S61: In-depth privacy risk detection for adversarial testing. Specifically, a trap question is simulated using a natural language template. The trap question is input into the financial analysis model, and all responses of the financial analysis model are captured. The responses are scanned using regular expressions, and sensitive information is marked once it is found. The leaked sensitive information is converted into a privacy vulnerability heatmap according to type and frequency, and the privacy vulnerability heatmap is used as a compliance score. Step S62: Deploy a lightweight financial analysis model to address the computing power bottleneck in real-time monitoring of the financial analysis model. Specifically, use an FPGA chip to compile the trap problem into an instruction stream, thereby accelerating the review of sensitive information. Step S63: Optimize the questioning pattern to locate sensitive information in the response and trace the source of the vulnerability. Specifically, the response is split word by word, sensitive information is marked, and through the attention mechanism, the words in the trap questions that the financial analysis model will focus on when the response contains sensitive information are analyzed. These words are recorded as prompt words. The prompt words that trigger sensitive information are traced back to the prompt words, and the prompt words and the corresponding trap question context are associated to form a traceability chain from the prompt words to the response containing sensitive information, so as to find dangerous questioning patterns. Step S64: Graded privacy hardening, specifically, classifying dangerous questioning patterns according to the severity of sensitive information leakage, implementing privacy hardening on the response of the financial analysis model, and achieving graded response; Step S65: The privacy-enhanced financial analysis model outputs financial assessment results.

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