Multi-business data analysis system for financial marketing service of energy industry

Abstract

The invention relates to the technical field of financial science and technology, in particular to a multi-business data analysis system for energy industry financial marketing service. Comprising a data acquisition and preprocessing unit which is used for acquiring multi-source financial data and preprocessing the acquired data; the data analysis and modeling unit is used for mining and analyzing the preprocessed data by applying various analysis methods and machine learning technologies, and constructing a prediction and decision model to support marketing decision; the marketing strategy optimization and execution unit is used for establishing a decision-making system through real-time data analysis and an intelligent algorithm, and adjusting and executing a marketing strategy in real time according to market changes and client feedback; the risk management and compliance unit is used for constructing a financial knowledge graph; and the system integration and operation and maintenance unit is used for integrating all units of the system to realize collaboration. According to the invention, a dynamic and adaptive marketing system is formed, and market changes and customer feedback can be quickly responded, so that the marketing effect and the rate of return on investment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fintech, and specifically to a multi-service data analysis system for financial marketing services in the energy industry. Background Art

[0002] In the current complex and ever-changing financial market environment, as a large energy enterprise, the power grid's financial business covers multiple fields such as funds, insurance brokerage, and financial leasing, playing an important role in the strategic development and business expansion of the enterprise. With the continuous development of the power grid's financial business, how to integrate and analyze multi-source business data to optimize marketing strategies, enhance customer experience, and increase revenue has become a key issue it faces. Therefore, a multi-service data analysis system for financial marketing services is needed. The multi-service data analysis system for financial marketing services aims to help financial institutions optimize marketing strategies, enhance customer experience, and increase revenue by integrating and analyzing multi-source business data. This system can process data from different business lines and provide real-time data analysis and visualization reports. In the prior art, the market changes rapidly, and it is difficult for marketing activities to be adjusted in a timely manner based on real-time feedback, missing market opportunities. Moreover, nowadays, financial business innovations are continuous, and new types of risks emerge in an endless stream. Traditional risk identification methods are difficult to cover all risks.

[0003] Based on this, the present invention provides a multi-service data analysis system for financial marketing services in the energy industry to solve the above-mentioned technical problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-service data analysis system for financial marketing services in the energy industry. The present invention forms a dynamic and adaptive marketing system by a real-time decision-making module that dynamically adjusts marketing strategies based on real-time data analysis and intelligent algorithms, a marketing automation module that selects appropriate marketing channels to execute activities, and an effect evaluation module that monitors in real time and calculates the return on investment, which can quickly respond to market changes and customer feedback, thereby improving marketing effectiveness and return on investment.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] The present invention provides a multi-service data analysis system for financial marketing services in the energy industry, including a data collection and preprocessing unit, a data analysis and modeling unit, a marketing strategy optimization and execution unit, a risk management and compliance unit, and a system integration and operation and maintenance unit, wherein:

[0007] The data collection and preprocessing unit: is used for collecting multi-source financial data and preprocessing the collected data;

[0008] The data analysis and modeling unit: It is used to mine and analyze the preprocessed data by using various analysis methods and machine learning technologies, and construct prediction and decision-making models to support marketing decisions;

[0009] The marketing strategy optimization and execution unit: It is used to establish a decision-making system through real-time data analysis and intelligent algorithms, and adjust and execute marketing strategies in real time according to market changes and customer feedback;

[0010] The risk management and compliance unit: It is used to construct a financial knowledge graph, combine machine learning algorithms, and automatically identify, evaluate, and control financial marketing risks;

[0011] The system integration and operation and maintenance unit: It is used to integrate each unit of the system to achieve collaboration, and is responsible for daily operation and maintenance, security guarantee, and system upgrade and optimization.

[0012] The data collection and preprocessing unit includes a data collection module, a data cleaning module, and a data storage module.

[0013] The data collection module is used to collect data from multi-source financial systems and external data sources; the data cleaning module is used to perform operations such as duplicate removal, missing value processing, and format standardization on the collected raw data; the data storage module is used to store the cleaned data in a distributed database or data warehouse.

[0014] The data analysis and modeling unit includes a segmentation module, a predictive analysis module, a recommendation system module, and a visualization module, where:

[0015] The segmentation module is used to divide customers into different groups through customer behavior, transaction data, etc.; the predictive analysis module is used to predict customer behavior using machine learning models, including customer churn rate, the likelihood of participating in funds, the tendency to purchase insurance products, and the probability of demand for financial leasing business; the recommendation system module is used to recommend personalized financial products based on customer historical behavior and preferences; the visualization module is used to display analysis results in the form of dashboards and reports.

[0016] The prediction of customer behavior using machine learning models, including customer churn rate, the likelihood of participating in funds, the tendency to purchase insurance products, and the probability of demand for financial leasing business, and the specific formula based on the regression algorithm is:

[0017] y = β0 + β1x + ∈

[0018] Among them, y is the predicted target variable, such as the customer churn rate or the probability of participating in a fund after a certain quantification process, the propensity to purchase insurance products, the numerical value of the demand probability for financial leasing business; x is the independent variable, such as a single characteristic of the customer's asset size, industry attribute, operating years, etc.; β0 is the intercept term, representing the value of y when x = 0; β1 is the regression coefficient, reflecting the degree of influence of x on y; ∈ is the error term, used to represent the part that the model cannot accurately fit.

[0019] The marketing strategy optimization and execution unit includes a real-time decision-making module, a marketing automation module, and an effect evaluation module, where:

[0020] The real-time decision-making module dynamically adjusts the marketing strategy based on real-time data analysis and intelligent algorithms; the marketing automation module is used to select appropriate marketing channels according to the formulated marketing strategy; the effect evaluation module is used to monitor the effect of marketing activities in real time and calculate the return on investment.

[0021] The real-time decision-making module dynamically adjusts the marketing strategy based on real-time data analysis and intelligent algorithms. The customer value scoring formula is:

[0022] C = α1R + α2F + α3M + α4S + α5I

[0023] Among them, C is the customer value score; R is the time interval since the last participation in a fund, insurance brokerage, or financial leasing business; F is the frequency of participation in a fund, insurance brokerage, or financial leasing business; M is the amount scale of participation in a fund, insurance brokerage, or financial leasing business; S is the strategic importance score of the customer in the power grid business ecosystem; I is the industry influence score of the customer, such as industry status, brand awareness; α1, α2, α3, α4, α5 are weight coefficients, adjusted according to business requirements..

[0024] The risk management and compliance unit includes a financial knowledge graph construction module, a risk identification module, a risk assessment module, and a risk control and compliance management module, where:

[0025] The financial knowledge graph construction module is used to collect various entity information in the financial field and construct a financial knowledge graph using graph database technology; the risk identification module is used to automatically identify potential risks in financial marketing activities by combining the financial knowledge graph and machine learning algorithms; the risk assessment module is used to quantitatively evaluate the identified risks and determine the level and impact degree of the risks; the risk control and compliance management module is used to formulate corresponding risk control strategies according to the risk assessment results and ensure that financial marketing activities comply with relevant laws, regulations, and regulatory requirements.

[0026] The risk identification module is used to automatically identify potential risks in financial marketing activities by combining a financial knowledge graph and machine learning algorithms. The specific formula based on the logistic regression algorithm is as follows:

[0027]

[0028] Among them, P(y = 1|X) is the probability of the occurrence of a risk event, where y = 1 indicates the occurrence of a risk and y = 0 indicates the non-occurrence of a risk; X = (x1, x1,..., x1) is the input feature vector such as customer credit score, transaction frequency, market volatility, etc.; β0, β1,..., β n are model parameters obtained by learning from training data; e is the natural constant, approximately equal to 2.718.

[0029] The system integration and operation and maintenance unit includes a system integration module, an operation and maintenance monitoring module, a security guarantee module, and an upgrade and optimization module. Among them: The system integration module is used to achieve seamless integration of each unit through a standardized interface to ensure the coordinated operation of data flow and business flow; The operation and maintenance monitoring module is used to monitor the hardware devices, software systems, network connections, etc. of the system in real time and collect the performance indicators of the system; The security guarantee module is used to provide multi-level security protection to ensure the security of the system; The upgrade and optimization module is used to evaluate the system regularly and formulate a system upgrade plan according to business requirements and technological development trends.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0031] 1. Through the real-time decision-making module, the present invention dynamically adjusts marketing strategies based on real-time data analysis and intelligent algorithms. The marketing automation module selects appropriate marketing channels to execute activities, and the effect evaluation module monitors in real time and calculates the return on investment, forming a dynamic and adaptive marketing system that can quickly respond to market changes and customer feedback, thereby improving marketing effectiveness and return on investment.

[0032] 2. The present invention constructs a comprehensive financial knowledge graph through the financial knowledge graph construction module. The risk identification module combines the graph and algorithms to automatically identify potential risks. The risk assessment module quantifies the risk level and impact degree, and the risk control and compliance management module formulates strategies and ensures compliance with regulatory requirements, effectively reducing various risks in financial marketing activities and ensuring the compliance operation of the business. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a system diagram of the multi-business data analysis system for energy industry financial marketing services of the present invention.

[0034] Figure 2 It is a system diagram of the data collection and preprocessing unit in the multi-business data analysis system for energy industry financial marketing services of the present invention.

[0035] Figure 3 This is a system diagram of the data analysis and modeling unit in the multi-service data analysis system for energy industry financial marketing services of the present invention.

[0036] Figure 4 This is a system diagram of the marketing strategy optimization and execution unit in the multi-service data analysis system for energy industry financial marketing services of the present invention.

[0037] Figure 5 This is a system diagram of the risk management and compliance unit in the multi-service data analysis system for energy industry financial marketing services of the present invention.

[0038] Figure 6 This is a system diagram of the system integration and operation and maintenance unit in the multi-service data analysis system for energy industry financial marketing services of the present invention.

[0039] Explanation of the reference numerals in the drawings:

[0040] 100, data collection and preprocessing unit; 101, data collection module; 102, data cleaning module; 103, data storage module; 200, data analysis and modeling unit; 201, subdivision module; 202, predictive analysis module; 203, recommendation system module; 204, visualization module; 300, marketing strategy optimization and execution unit; 301, real-time decision-making module; 302, marketing automation module; 303, effect evaluation module; 400, risk management and compliance unit; 401, financial knowledge graph construction module; 402, risk identification module; 403, risk assessment module; 404, risk control and compliance management module; 500, system integration and operation and maintenance unit; 501, system integration module; 502, operation and maintenance monitoring module; 503, security guarantee module; 504, upgrade and optimization module. Detailed implementation manners

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] Embodiment:

[0043] Such as Figures 1-6As shown in the figure, this embodiment provides a multi-service data analysis system for energy industry financial marketing services, including a data collection and preprocessing unit 100, a data analysis and modeling unit 200, a marketing strategy optimization and execution unit 300, a risk management and compliance unit 400, and a system integration and operation and maintenance unit 500, where:

[0044] The data collection and preprocessing unit 100: is used to collect multi-source financial data and preprocess the collected data;

[0045] The data analysis and modeling unit 200: is used to apply various analysis methods and machine learning technologies to mine and analyze the preprocessed data, and construct prediction and decision models to support marketing decisions;

[0046] The marketing strategy optimization and execution unit 300: is used to establish a decision-making system through real-time data analysis and intelligent algorithms, and adjust and execute marketing strategies in real time according to market changes and customer feedback;

[0047] The risk management and compliance unit 400: is used to construct a financial knowledge graph, combined with machine learning algorithms, to automatically identify, evaluate, and control financial marketing risks;

[0048] The system integration and operation and maintenance unit 500: is used to integrate each unit of the system to achieve collaboration, and is responsible for daily operation and maintenance, security guarantee, and system upgrade and optimization.

[0049] The data collection and preprocessing unit 100 includes a data collection module 101, a data cleaning module 102, and a data storage module 103.

[0050] The data collection module 101 is used to collect data from multi-source financial systems and external data sources; the data cleaning module 102 is used to perform operations such as deduplication, missing value processing, and format standardization on the collected raw data; the data storage module 103 is used to store the cleaned data in a distributed database or data warehouse.

[0051] The data analysis and modeling unit 200 includes a segmentation module 201, a predictive analysis module 202, a recommendation system module 203, and a visualization module 204, where:

[0052] The segmentation module 201 is used to divide customers into different groups through customer behavior, transaction data, etc.; the predictive analysis module 202 is used to predict customer behavior using machine learning models, including customer churn rate, likelihood of participating in funds, propensity to purchase insurance products, and probability of demand for financial leasing business; the recommendation system module 203 is used to recommend personalized financial products according to customer historical behavior and preferences; the visualization module 204 is used to display analysis results in the form of dashboards and reports.

[0053] Predicting customer behavior using machine learning models includes customer churn rate, the likelihood of participating in funds, the propensity to purchase insurance products, and the probability of demand for financial leasing services. The specific formula based on the regression algorithm is as follows:

[0054] y = β0 + β1x + ∈

[0055] Among them, y is the predicted target variable, such as the customer churn rate or the value of the likelihood of participating in funds, the propensity to purchase insurance products, and the probability of demand for financial leasing services after a certain quantitative processing; x is the independent variable, such as single features like the customer's asset size, industry attribute, and business operation years; β0 is the intercept term, representing the value of y when x = 0; β1 is the regression coefficient, reflecting the influence degree of x on y; ∈ is the error term, used to represent the part that the model cannot accurately fit.

[0056] The marketing strategy optimization and execution unit 300 includes a real-time decision-making module 301, a marketing automation module 302, and an effect evaluation module 303, where:

[0057] The real-time decision-making module 301 dynamically adjusts the marketing strategy based on real-time data analysis and intelligent algorithms; the marketing automation module 302 is used to select appropriate marketing channels according to the formulated marketing strategy; the effect evaluation module 303 is used to monitor the effect of marketing activities in real time and calculate the return on investment.

[0058] The real-time decision-making module 301 dynamically adjusts the marketing strategy based on real-time data analysis and intelligent algorithms. The customer value scoring formula is as follows:

[0059] C = α1R + α2F + α3M + α4S + α5I

[0060] Among them, C is the customer value score; R is the time interval since the last participation in funds, insurance brokerage, or financial leasing services; F is the frequency of participation in funds, insurance brokerage, or financial leasing services; M is the amount scale of participation in funds, insurance brokerage, or financial leasing services; S is the strategic importance score of the customer in the power grid business ecosystem; I is the industry influence score of the customer, such as industry status and brand awareness; α1, α2, α3, α4, α5 are weight coefficients, which are adjusted according to business needs.

[0061] The risk management and compliance unit 400 includes a financial knowledge graph construction module 401, a risk identification module 402, a risk assessment module 403, and a risk control and compliance management module 404, where: The financial knowledge graph construction module 401 is used to collect various entity information in the financial field and construct a financial knowledge graph by using graph database technology; The risk identification module 402 is used to automatically identify potential risks in financial marketing activities by combining the financial knowledge graph and machine learning algorithms; The risk assessment module 403 is used to quantitatively evaluate the identified risks and determine the level and impact degree of the risks; The risk control and compliance management module 404 is used to formulate corresponding risk control strategies according to the risk assessment results and ensure that financial marketing activities comply with relevant laws, regulations and regulatory requirements. The risk identification module 402 is used to automatically identify potential risks in financial marketing activities by combining the financial knowledge graph and machine learning algorithms. The specific formula based on the logistic regression algorithm is;

[0062]

[0063] Among them, P(y = 1|X) is the probability of the occurrence of a risk event, y = 1 indicates the occurrence of a risk, and y = 0 indicates the non-occurrence of a risk; X = (x1, x1,..., x1) is an input feature vector such as customer credit score, transaction frequency, market volatility, etc.; β0, β1,..., β n are model parameters obtained by learning from training data; e is the natural constant, approximately equal to 2.718.

[0064] The system integration and operation and maintenance unit 500 includes a system integration module 501, an operation and maintenance monitoring module 502, a security guarantee module 503, and an upgrade and optimization module 504, where: The system integration module 501 is used to achieve seamless integration of each unit through standardized interfaces and ensure the coordinated operation of data streams and business flows; The operation and maintenance monitoring module 502 is used to monitor the hardware devices, software systems, network connections, etc. of the system in real time and collect the performance indicators of the system; The security guarantee module 503 is used to provide multi-level security protection to ensure system security; The upgrade and optimization module 504 is used to evaluate the system regularly and formulate a system upgrade plan according to business requirements and technological development trends.

[0065] In this embodiment, for the multi-business data analysis system for energy industry financial marketing services, the specific method is as follows: First, the data collection module 101 is used to collect data from multi-source financial systems and external data sources; the multi-source financial systems include the CRM system and the trading system; the external data sources include social media and market data. The data cleaning module 102 is used to perform operations such as deduplication, missing value processing, and format standardization on the collected raw data; ensuring the consistency and accuracy of the data. The data storage module 103 is used to store the cleaned data in a distributed database or a data warehouse. The segmentation module 201 is used to divide customers into different groups through customer behavior, transaction data, etc.; different groups: such as high-value customers and potential churn customers. The predictive analysis module 202 is used to predict customer behavior using machine learning models, including customer churn rate, the likelihood of participating in funds, the tendency to purchase insurance products, and the probability of demand for financial leasing services; the recommendation system module 203 is used to recommend personalized financial products based on customer historical behavior and preferences; the visualization module 204 is used to display the analysis results in the form of dashboards and reports. Second, using machine learning models to predict customer behavior, including customer churn rate, the likelihood of participating in funds, the tendency to purchase insurance products, and the probability of demand for financial leasing services, the specific formula based on the regression algorithm is:

[0066] y = β0 + β1x + ∈

[0067] Among them, y is the predicted target variable, such as the customer churn rate or the value of the likelihood of participating in funds, the tendency to purchase insurance products, and the probability of demand for financial leasing services after some quantitative processing; x is the independent variable, such as a single feature of the customer's asset size, industry attribute, operating years, etc.; β0 is the intercept term, representing the value of y when x = 0; β1 is the regression coefficient, reflecting the influence degree of x on y; ∈ is the error term, used to represent the part that the model cannot accurately fit. The real-time decision-making module 301 dynamically adjusts the marketing strategy based on real-time data analysis and intelligent algorithms; the marketing automation module 302 is used to select appropriate marketing channels according to the formulated marketing strategy; marketing channels such as email, SMS, social media, and offline activities, and execute marketing activities. The effect evaluation module 303 is used to monitor the effect of marketing activities in real time and calculate the return on investment. The real-time decision-making module 301 dynamically adjusts the marketing strategy based on real-time data analysis and intelligent algorithms, and the customer value scoring formula is:

[0068] C = α1R + α2F + α3M + α4S + α5I

[0069] Among them, C is the customer value score; R is the time interval since the last participation in fund, insurance brokerage, or financial leasing business; F is the frequency of participation in fund, insurance brokerage, or financial leasing business; M is the amount scale of participation in fund, insurance brokerage, or financial leasing business; S is the strategic importance score of the customer in the power grid business ecosystem; I is the industry influence score of the customer, such as industry status and brand awareness; α1, α2, α3, α4, α5 are weight coefficients, which are adjusted according to business needs. The financial knowledge graph construction module 401 is used to collect various entity information in the financial field and construct a financial knowledge graph using graph database technology; the entity information includes customers, financial products, market events, and laws and regulations. The graph database technology is Neo4j. The risk identification module 402 is used to automatically identify potential risks in financial marketing activities by combining the financial knowledge graph and machine learning algorithms; the risks include credit risk, market risk, and operational risk. The risk assessment module 403 is used to quantitatively evaluate the identified risks and determine the level and impact of the risks; the risk control and compliance management module 404 is used to formulate corresponding risk control strategies based on the risk assessment results and ensure that financial marketing activities comply with relevant laws, regulations, and regulatory requirements. The risk control strategies include risk warning, risk mitigation, and risk transfer. The regulatory requirements are such as GDPR and anti-money laundering regulations. The risk identification module 402 is used to automatically identify potential risks in financial marketing activities by combining the financial knowledge graph and machine learning algorithms. The specific formula based on the logistic regression algorithm is;

[0070]

[0071] Among them, P(y = 1|X) is the probability of the occurrence of a risk event, y = 1 indicates the occurrence of a risk, and y = 0 indicates the non-occurrence of a risk; X = (x1, x1,..., x1) is the input feature vector such as customer credit score, transaction frequency, market volatility, etc.; β0, β1,..., β nare model parameters obtained through learning from training data; e is the natural constant, approximately equal to 2.718. Finally, the system integration module 501 is used to achieve seamless integration of each unit through a standardized interface, ensuring the coordinated operation of the data flow and business flow; interface technologies such as RESTful API and SOAP. The operation and maintenance monitoring module 502 is used to monitor the hardware devices, software systems, network connections, etc. of the system in real time and collect the performance indicators of the system; performance indicators such as CPU usage rate, memory usage rate, and network bandwidth. The security guarantee module 503 is used to provide multi-level security protection to ensure the security of the system; encryption technology is adopted to encrypt and store and transmit sensitive data to prevent data leakage. Implement access control policies to strictly manage the access rights of users to ensure that only authorized users can access the system and data. Establish a security audit mechanism to record the operation behaviors of users for easy security traceability and auditing. The upgrade and optimization module 504 is used to evaluate the system regularly and formulate a system upgrade plan according to the business requirements and technological development trends.

[0072] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0073] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not elaborate on all the details and do not limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A multi-service data analysis system for energy industry financial marketing services, characterized in that, It includes a data collection and preprocessing unit (100), a data analysis and modeling unit (200), a marketing strategy optimization and execution unit (300), a risk management and compliance unit (400), and a system integration and operation and maintenance unit (500), where: The data collection and preprocessing unit (100): is used for collecting multi-source financial data and preprocessing the collected data; The data analysis and modeling unit (200): is used for mining and analyzing the preprocessed data by using various analysis methods and machine learning technologies, and constructing prediction and decision-making models to support marketing decisions; The marketing strategy optimization and execution unit (300): is used for establishing a decision-making system through real-time data analysis and intelligent algorithms, and adjusting and executing marketing strategies in real time according to market changes and customer feedback; The risk management and compliance unit (400): is used for constructing a financial knowledge graph, combining machine learning algorithms, and automatically identifying, evaluating, and controlling financial marketing risks; The system integration and operation and maintenance unit (500): is used for integrating each unit of the system to achieve collaboration, and is responsible for daily operation and maintenance, security guarantee, and system upgrade and optimization.

2. The multi-service data analysis system for energy industry financial marketing services according to claim 1, wherein The data collection and preprocessing unit (100) includes a data collection module (101), a data cleaning module (102), and a data storage module (103).

3. The multi-service data analysis system for energy industry financial marketing services according to claim 2, wherein, The data collection module (101) is used for collecting data from multi-source financial systems and external data sources; the data cleaning module (102) is used for performing operations such as duplicate removal, missing value processing, and format standardization on the collected raw data; the data storage module (103) is used for storing the cleaned data into a distributed database or a data warehouse.

4. The multi-service data analysis system for energy industry financial marketing services according to claim 1, characterized in that, The data analysis and modeling unit (200) includes a segmentation module (201), a predictive analysis module (202), a recommendation system module (203), and a visualization module (204), where: The segmentation module (201) is used for dividing customers into different groups through customer behavior, transaction data, etc.; the predictive analysis module (202) is used for predicting customer behavior by using machine learning models, including customer churn rate, the likelihood of participating in funds, the tendency to purchase insurance products, and the probability of demand for financial leasing business; the recommendation system module (203) is used for recommending personalized financial products according to customer historical behavior and preferences; the visualization module (204) is used for displaying the analysis results in the form of dashboards and reports.

5. The multi-service data analysis system for energy industry financial marketing services according to claim 4, wherein The prediction of customer behavior by using machine learning models, including customer churn rate, the likelihood of participating in funds, the tendency to purchase insurance products, and the probability of demand for financial leasing business, and the specific formula based on the regression algorithm is: y = β0 + β1x + ∈ Among them, y is the predicted target variable, such as the customer churn rate or the probability of participating in a fund after a certain quantitative processing, the propensity to purchase insurance products, the numerical value of the demand probability for financial leasing business; x is the independent variable, such as a single feature of the customer's asset size, industry attribute, business operation years, etc.; β0 is the intercept term, representing the value of y when x = 0; β1 is the regression coefficient, reflecting the influence degree of x on y; ∈ is the error term, used to represent the part that the model cannot accurately fit.

6. The multi-service data analysis system for energy industry financial marketing services according to claim 1, characterized in that, The marketing strategy optimization and execution unit (300) includes a real-time decision-making module (301), a marketing automation module (302), and an effect evaluation module (303), where: The real-time decision-making module (301) dynamically adjusts the marketing strategy based on real-time data analysis and intelligent algorithms; the marketing automation module (302) is used to select appropriate marketing channels according to the formulated marketing strategy; the effect evaluation module (303) is used to monitor the effect of marketing activities in real time and calculate the return on investment.

7. The multi-service data analysis system for energy industry financial marketing services according to claim 6, characterized in that, The real-time decision-making module (301) dynamically adjusts the marketing strategy based on real-time data analysis and intelligent algorithms. The customer value scoring formula is: C = α1R + α2F + α3M + α4S + α5I Among them, C is the customer value score; R is the time interval since the last participation in a fund, insurance brokerage, or financial leasing business; F is the frequency of participating in a fund, insurance brokerage, or financial leasing business; M is the amount scale of participating in a fund, insurance brokerage, or financial leasing business; S is the strategic importance score of the customer in the power grid business ecosystem; I is the industry influence score of the customer, such as industry status, brand awareness; α1, α2, α3, α4, α5 are weight coefficients, adjusted according to business requirements.

8. The multi-service data analysis system for energy industry financial marketing services according to claim 1, characterized in that, The risk management and compliance unit (400) includes a financial knowledge graph construction module (401), a risk identification module (402), a risk assessment module (403), and a risk control and compliance management module (404), where: The financial knowledge graph construction module (401) is used to collect various entity information in the financial field and construct a financial knowledge graph using graph database technology; the risk identification module (402) is used to automatically identify potential risks in financial marketing activities by combining the financial knowledge graph and machine learning algorithms; the risk assessment module (403) is used to quantitatively evaluate the identified risks and determine the level and impact degree of the risks; the risk control and compliance management module (404) is used to formulate corresponding risk control strategies according to the risk assessment results and ensure that financial marketing activities comply with relevant laws, regulations, and regulatory requirements.

9. The multi-service data analysis system for energy industry financial marketing services according to claim 8, characterized in that, The risk identification module (402) is used to automatically identify potential risks in financial marketing activities by combining the financial knowledge graph and machine learning algorithms. The specific formula based on the logistic regression algorithm is; Among them, P(y = 1|X) is the probability of the occurrence of a risk event, where y = 1 indicates the occurrence of the risk and y = 0 indicates the non-occurrence of the risk; X = (x1, x1, …, x1) is the input feature vector such as customer credit score, transaction frequency, market volatility, etc.; β0, β1, …, β n are model parameters obtained by learning from training data; e is the natural constant, approximately equal to 2.

718.

10. The multi-service data analysis system for energy industry financial marketing services according to claim 1, wherein The system integration and operation and maintenance unit (500) includes a system integration module (501), an operation and maintenance monitoring module (502), a security guarantee module (503), and an upgrade and optimization module (504), where: The system integration module (501) is used to achieve seamless integration of each unit through a standardized interface to ensure the coordinated operation of data streams and business processes; the operation and maintenance monitoring module (502) is used to monitor the system's hardware devices, software systems, network connections, etc. in real time and collect the system's performance metrics; the security guarantee module (503) is used to provide multi-level security protection to ensure system security; the upgrade and optimization module (504) is used to evaluate the system regularly and formulate a system upgrade plan according to business requirements and technological development trends.

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