Power trading platform safety early warning method and system based on user behaviors

Abstract

The invention relates to the technical field of electric power system application, in particular to an electric power transaction platform safety early warning method and system based on user behaviors, and the method comprises the steps: collecting various transaction data of a user on a platform, building a corresponding database, carrying out the preprocessing and analysis of the collected user behavior data, obtaining useful feature information, and carrying out the early warning of the user behavior. All transaction businesses are ensured to accord with laws and regulations and market transaction rules published by the government and published by later optimization and rectification, and various risk disputes are prevented. The power transaction platform has the advantage of good safety early warning effect through the power transaction platform safety early warning capability for the user behaviors, and solves the problems that with the promotion of power informatization, the intranet user behavior abnormity becomes one of important factors causing power information safety accidents, and the user behavior abnormity cannot be early warned in time, so that the power information safety accidents are caused. The problems that the transaction error fund of the market main body is large, and the security of the power transaction platform, the user information and the fund is not well guaranteed are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system application, and specifically to a user behavior-based power trading platform security early warning method and system. Background Art

[0002] With the deepening of the reform of the power trading system, information systems are playing an increasingly important role in the power trading market. As an important part of the power market, the security of the power trading platform is crucial to ensuring the stable operation of the market.

[0003] At present, with the advancement of power informatization, abnormal behavior of intranet users has become one of the important factors leading to power information security accidents. Since abnormal user behavior cannot be warned in time, market entities suffer large transaction errors and the security of power trading platforms and user information and funds is poor. For this reason, we propose a power trading platform security warning method and system based on user behavior. Summary of the invention

[0004] The purpose of the present invention is to provide a security warning method and system for an electricity trading platform based on user behavior, which has the advantage of good security warning effect and solves the problem that with the advancement of power informatization, abnormal behavior of intranet users has become one of the important factors leading to power information security accidents. However, since abnormal user behavior cannot be warned in time, market entities suffer large transaction errors and suffer poor security of power trading platforms and user information and funds.

[0005] To achieve the above object, the present invention provides the following technical solution: a safety early warning method for a power trading platform based on user behavior, the method comprising:

[0006] Step S100: Collect various transaction data of users on the platform and establish a corresponding database;

[0007] Step S200: pre-processing and analyzing the collected user behavior data to obtain useful feature information;

[0008] Step S300: Ensure that all transactions comply with laws and regulations and market transaction rules that have been issued by the government and those that have been optimized and rectified later, to prevent various risks and disputes;

[0009] Step S400: constructing an early warning model and using a similarity analysis algorithm to calculate the similarity of user behavior based on the difference between the user's real-time behavior and the normal behavior pattern, and comparing it with the normal user behavior to determine whether there is a security risk;

[0010] Step S500: Monitor user behavior in real time, use the early warning system to predict potential security risks, and take corresponding measures when necessary;

[0011] Step S600: As user behavior changes and new security threats emerge, the early warning system needs to be continuously optimized and updated to ensure its accuracy and effectiveness;

[0012] Preferably, the step S100 includes:

[0013] Step S101: Collecting user operation data, including all user activities on the power trading platform, such as login time, transaction frequency and transaction amount, operation path and browsing behavior data;

[0014] Step S102: Collecting user behavior data such as the user's electricity consumption, electricity consumption time, and electricity consumption frequency, the user's electricity expenditure, consumption data of payment records, the user's real-time or predicted load data, the user's credit record, credit data of payment behavior, the user's loyalty to the power supplier, stickiness data such as usage time, and environmental protection data such as the user's energy use efficiency and environmental protection behavior;

[0015] Step S103: sorting various data of users of the power information intranet and building a database of user behavior;

[0016] Preferably, the step S200 includes:

[0017] Step S201: Data cleaning. For missing data, methods such as ignoring missing data, deleting negative values, filling with constant values, filling with overall mean values, filling with most likely values, and filling with regression methods can be used for processing. Data reduction can be performed by direct deletion, chi-square test, information gain, information gain rate, and other methods to reduce the size of the database while retaining key information. Data can be converted by methods such as decimal calibration, maximum and minimum values, and Z-SCORE to better adapt to subsequent data analysis models.

[0018] Step S201: Data mining, using ID3 decision tree and analytic hierarchy process, can effectively classify and cluster the user behavior data, thereby discovering patterns and associations in the data;

[0019] Step S203: Feature extraction. Extracting useful features from user behavior data is the key to analysis, such as browsing time, purchase frequency, number of clicks, etc. These features can be used to describe user behavior patterns and interests.

[0020] Preferably, the step S300 includes:

[0021] Step S301: Strengthen supervision. The supervision of the power trading platform should follow the principles of openness, fairness and efficiency to ensure that market members consciously abide by relevant laws and regulations. The supervision targets include power trading entities, power market operating institutions and power grid companies that provide transmission and distribution services;

[0022] Step S302: Improve the supervision content, which should include the fulfillment of power system safety obligations, entry and exit from the power market, qualifications for participating in power market transactions, implementation of power market operation rules, transactions and electricity fee settlement, information disclosure, implementation of national standards and industry standards, and balance of fund management and fund use, etc., and introduce innovative supervision methods. With the deepening of the low-carbon transformation of the energy structure, the safety, reliability and real-time performance of the new power system are facing challenges. The power market supervision method should introduce innovative methods such as third-party agency business evaluation, supervision information system application and credit supervision mechanism to adapt to the new situation of power market development;

[0023] Step S303: clarify the rights and responsibilities, create a fair trading guarantee mechanism, standardize the responsibilities of power market entities including power trading entities, power market operating institutions and power grid enterprises, constrain market access, exit and intervention behaviors, clarify the responsibilities of regulatory agencies and their implementation methods, prevent unfair competition in the market, and strengthen information disclosure. The operation of the power trading platform should be based on relatively independent trading centers within the management areas of the State Grid and the Southern Power Grid, lay the foundation for information disclosure of power market transactions, and ensure the transaction quality of the power market;

[0024] Step S303: Strengthen network security management. In the face of network security challenges in the power industry, a network security management agency should be established in the power industry, network security infrastructure construction and awareness education should be strengthened in the power industry, a network security emergency response team should be established, risk assessment and monitoring should be carried out, and advanced network security protection technologies should be adopted to effectively ensure that all transaction businesses comply with laws and regulations, prevent various risk disputes, and thus maintain the order of the power market and the interests of participants;

[0025] Preferably, the step S400 includes:

[0026] Step S401: Select an appropriate modeling method, such as moving average, autoregression, ARIMA, GARCH, etc., according to the characteristics of the collected and analyzed user behavior data, to predict future electricity consumption trends;

[0027] Step S402: Model training and validation, using historical data to train the model, and evaluating the accuracy and stability of the model through methods such as cross-validation;

[0028] Step S403: Model deployment and monitoring: deploy the trained model on the power trading platform, monitor the difference between the user's power consumption behavior and the normal behavior pattern in real time, use the similarity analysis algorithm to calculate the similarity of user behavior, and compare it with the normal user behavior. Once abnormal behavior or potential risk is found, the early warning mechanism is triggered immediately;

[0029] Preferably, the step S500 includes:

[0030] Step S501: Combine advanced network security technology to prevent and control risk points in the entire platform transaction process, including a complete rule system, advanced identity authentication mechanism, etc., and strengthen data security management, establish a complete data encryption mechanism, ensure the security of data during transmission and storage, and protect user data from unauthorized access and leakage by adopting advanced encryption technology, and strictly manage data access rights to prevent unauthorized access and leakage. By implementing access control strategies, ensure that only authorized users can access sensitive data, thereby reducing the risk of data leakage;

[0031] Step S502: Establish a comprehensive monitoring and early warning mechanism to promptly discover and resolve potential problems and prevent system failures or crashes;

[0032] Step S503: By monitoring user behavior in real time, the early warning system can predict potential security risks and take corresponding measures when necessary, such as restricting account access, freezing transactions, etc., to reduce potential losses;

[0033] Preferably, the step S600 includes:

[0034] Step S601: With the development of network technology and the continuous changes in attack methods, the security monitoring and early warning system needs to be continuously updated and upgraded to adapt to new security threats. At the same time, the security monitoring and early warning system is integrated with other network security tools and technologies to achieve information sharing and collaborative work, and improve the overall security protection capabilities;

[0035] Step S602: Strengthen the construction and training of the security team, improve the professional skills and response capabilities of personnel, ensure the effective implementation of security monitoring and early warning work, and introduce new technologies, use big data, artificial intelligence and other technologies to conduct in-depth analysis of user behavior, and improve the accuracy and real-time performance of anomaly detection;

[0036] Step S603: Adjust and optimize the model according to the warning effect, including parameter adjustment, feature selection, etc., to improve the accuracy and timeliness of the warning, and continuously monitor and update.

[0037] In order to better implement the above method, a safety early warning system for the power trading platform based on user behavior is also proposed. The system includes a user behavior information collection module, a user behavior data analysis module, a legal guarantee module, a database, a machine learning model construction module, a risk prediction and response module, and a continuous optimization and update module;

[0038] The user behavior information collection module is used to collect various transaction data of users on the platform and establish a corresponding database;

[0039] The user behavior data analysis module is used to pre-process and analyze the collected user behavior data to obtain useful feature information;

[0040] The legal guarantee module is used to ensure that all transactions comply with laws and regulations and market transaction rules that have been issued by the government and those that have been optimized and rectified later, to prevent various risk disputes;

[0041] The machine learning model building module is used to build an early warning model and calculate the similarity of user behavior based on the difference between the user's real-time behavior and the normal behavior pattern using a similarity analysis algorithm, and compare it with the normal user behavior to determine whether there is a security risk;

[0042] The risk prediction and response module is used to monitor user behavior in real time, predict potential security risks using the early warning system, and take corresponding measures when necessary;

[0043] The continuous optimization and update module needs to continuously optimize and update the early warning system as user behavior changes and new security threats emerge to ensure its accuracy and effectiveness;

[0044] The database is used to provide data support and data comparison to the machine learning model construction module, the user behavior data analysis module, the risk prediction and response module, and the continuous optimization and update module, and can store and update data. The machine learning model construction module can provide data support to the risk prediction and response module to ensure the timeliness and accuracy of risk prediction and response.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention monitors various transaction data of users on the platform in real time and establishes a corresponding database. After cleaning the collected data, the data is deeply mined to extract useful features from the user behavior data. After ensuring that all transaction businesses comply with laws and regulations and market transaction rules that have been issued by the government and later optimized and rectified, an early warning model is constructed, and the trained model is deployed on the power trading platform to monitor the difference between the user's power consumption behavior and the normal behavior pattern in real time. The similarity of the user behavior is calculated by using a similarity analysis algorithm and compared with the normal user behavior. Once abnormal behavior or potential risk is found, the early warning mechanism is immediately triggered. The early warning system can predict potential security risks and take corresponding measures when necessary, such as restricting account access, freezing transactions, etc., to reduce potential losses, thereby ensuring the security of the power trading platform and user information and funds. At the same time, the model is adjusted and optimized according to the early warning effect, including parameter adjustment, feature selection, etc., to improve the accuracy and timeliness of the early warning, and continuous monitoring and updating enable the early warning to grow by itself and better serve the power trading platform and users. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the process of the present invention;

[0048] Figure 2 It is a schematic diagram of the system structure of the present invention; DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] See also Figure 1-Figure 2 As shown, the present invention provides a technical solution: a safety early warning method for a power trading platform based on user behavior, the method comprising:

[0051] Step S100: Collect various transaction data of users on the platform and establish a corresponding database;

[0052] Step S200: pre-processing and analyzing the collected user behavior data to obtain useful feature information;

[0053] Step S300: Ensure that all transactions comply with laws and regulations and market transaction rules that have been issued by the government and those that have been optimized and rectified later, to prevent various risks and disputes;

[0054] Step S400: constructing an early warning model and using a similarity analysis algorithm to calculate the similarity of user behavior based on the difference between the user's real-time behavior and the normal behavior pattern, and comparing it with the normal user behavior to determine whether there is a security risk;

[0055] Step S500: Monitor user behavior in real time, use the early warning system to predict potential security risks, and take corresponding measures when necessary;

[0056] Step S600: As user behavior changes and new security threats emerge, the early warning system needs to be continuously optimized and updated to ensure its accuracy and effectiveness;

[0057] Wherein, step S100 includes:

[0058] Step S101: Collecting user operation data, including all user activities on the power trading platform, such as login time, transaction frequency and transaction amount, operation path and browsing behavior data;

[0059] Step S102: Collecting user behavior data such as the user's electricity consumption, electricity consumption time, and electricity consumption frequency, the user's electricity expenditure, consumption data of payment records, the user's real-time or predicted load data, the user's credit record, credit data of payment behavior, the user's loyalty to the power supplier, stickiness data such as usage time, and environmental protection data such as the user's energy use efficiency and environmental protection behavior;

[0060] Step S103: sorting various data of users of the power information intranet and building a database of user behavior;

[0061] Wherein, step S200 includes:

[0062] Step S201: Data cleaning. For missing data, methods such as ignoring missing data, deleting negative values, filling with constant values, filling with overall mean values, filling with most likely values, and filling with regression methods can be used for processing. Data reduction can be performed by direct deletion, chi-square test, information gain, information gain rate, and other methods to reduce the size of the database while retaining key information. Data can be converted by methods such as decimal calibration, maximum and minimum values, and Z-SCORE to better adapt to subsequent data analysis models.

[0063] Step S201: Data mining, using ID3 decision tree and analytic hierarchy process, can effectively classify and cluster the user behavior data, thereby discovering patterns and associations in the data;

[0064] Step S203: Feature extraction. Extracting useful features from user behavior data is the key to analysis, such as browsing time, purchase frequency, number of clicks, etc. These features can be used to describe user behavior patterns and interests.

[0065] Wherein, step S300 includes:

[0066] Step S301: Strengthen supervision. The supervision of the power trading platform should follow the principles of openness, fairness and efficiency to ensure that market members consciously abide by relevant laws and regulations. The supervision targets include power trading entities, power market operating institutions and power grid companies that provide transmission and distribution services;

[0067] Step S302: Improve the supervision content, which should include the fulfillment of power system safety obligations, entry and exit from the power market, qualifications for participating in power market transactions, implementation of power market operation rules, transactions and electricity fee settlement, information disclosure, implementation of national standards and industry standards, and balance of fund management and fund use, etc., and introduce innovative supervision methods. With the deepening of the low-carbon transformation of the energy structure, the safety, reliability and real-time performance of the new power system are facing challenges. The power market supervision method should introduce innovative methods such as third-party agency business evaluation, supervision information system application and credit supervision mechanism to adapt to the new situation of power market development;

[0068] Step S303: clarify the rights and responsibilities, create a fair trading guarantee mechanism, standardize the responsibilities of power market entities including power trading entities, power market operating institutions and power grid enterprises, constrain market access, exit and intervention behaviors, clarify the responsibilities of regulatory agencies and their implementation methods, prevent unfair competition in the market, and strengthen information disclosure. The operation of the power trading platform should be based on relatively independent trading centers within the management areas of the State Grid and the Southern Power Grid, lay the foundation for information disclosure of power market transactions, and ensure the transaction quality of the power market;

[0069] Step S303: Strengthen network security management. In the face of network security challenges in the power industry, a network security management agency should be established in the power industry, network security infrastructure construction and awareness education should be strengthened in the power industry, a network security emergency response team should be established, risk assessment and monitoring should be carried out, and advanced network security protection technologies should be adopted to effectively ensure that all transaction businesses comply with laws and regulations, prevent various risk disputes, and thus maintain the order of the power market and the interests of participants;

[0070] Wherein, step S400 includes:

[0071] Step S401: Select an appropriate modeling method, such as moving average, autoregression, ARIMA, GARCH, etc., according to the characteristics of the collected and analyzed user behavior data, to predict future electricity consumption trends;

[0072] Step S402: Model training and validation, using historical data to train the model, and evaluating the accuracy and stability of the model through methods such as cross-validation;

[0073] Step S403: Model deployment and monitoring: deploy the trained model on the power trading platform, monitor the difference between the user's power consumption behavior and the normal behavior pattern in real time, use the similarity analysis algorithm to calculate the similarity of user behavior, and compare it with the normal user behavior. Once abnormal behavior or potential risk is found, the early warning mechanism is triggered immediately;

[0074] Wherein, step S500 includes:

[0075] Step S501: Combine advanced network security technology to prevent and control risk points in the entire platform transaction process, including a complete rule system, advanced identity authentication mechanism, etc., and strengthen data security management, establish a complete data encryption mechanism, ensure the security of data during transmission and storage, and protect user data from unauthorized access and leakage by adopting advanced encryption technology, and strictly manage data access rights to prevent unauthorized access and leakage. By implementing access control strategies, ensure that only authorized users can access sensitive data, thereby reducing the risk of data leakage;

[0076] Step S502: Establish a comprehensive monitoring and early warning mechanism to promptly discover and resolve potential problems and prevent system failures or crashes;

[0077] Step S503: By monitoring user behavior in real time, the early warning system can predict potential security risks and take corresponding measures when necessary, such as restricting account access, freezing transactions, etc., to reduce potential losses;

[0078] Wherein, step S600 includes:

[0079] Step S601: With the development of network technology and the continuous changes in attack methods, the security monitoring and early warning system needs to be continuously updated and upgraded to adapt to new security threats. At the same time, the security monitoring and early warning system is integrated with other network security tools and technologies to achieve information sharing and collaborative work, and improve the overall security protection capabilities;

[0080] Step S602: Strengthen the construction and training of the security team, improve the professional skills and response capabilities of personnel, ensure the effective implementation of security monitoring and early warning work, and introduce new technologies, use big data, artificial intelligence and other technologies to conduct in-depth analysis of user behavior, and improve the accuracy and real-time performance of anomaly detection;

[0081] Step S603: Adjust and optimize the model according to the warning effect, including parameter adjustment, feature selection, etc., to improve the accuracy and timeliness of the warning, and continuously monitor and update.

[0082] In order to better implement the above method, a safety early warning system for the power trading platform based on user behavior is also proposed. The system includes a user behavior information collection module, a user behavior data analysis module, a legal guarantee module, a database, a machine learning model construction module, a risk prediction and response module, and a continuous optimization and update module;

[0083] The user behavior information collection module is used to collect various transaction data of users on the platform and establish a corresponding database;

[0084] The user behavior data analysis module is used to pre-process and analyze the collected user behavior data to obtain useful feature information;

[0085] The legal guarantee module is used to ensure that all transactions comply with laws and regulations and the market transaction rules that have been issued by the government and those that have been optimized and rectified later, to prevent various risks and disputes;

[0086] The machine learning model building module is used to build an early warning model and use a similarity analysis algorithm to calculate the similarity of user behavior based on the difference between the user's real-time behavior and normal behavior patterns, and compare it with normal user behavior to determine whether there is a security risk;

[0087] The risk prediction and response module is used to monitor user behavior in real time, use the early warning system to predict potential security risks, and take corresponding measures when necessary;

[0088] Continuous optimization and update modules As user behavior changes and new security threats emerge, the early warning system needs to be continuously optimized and updated to ensure its accuracy and effectiveness;

[0089] The database is used to provide data support and data comparison to the machine learning model construction module, user behavior data analysis module, risk prediction and response module, and continuous optimization and update module, and can store and update data. The machine learning model construction module can provide data support to the risk prediction and response module to ensure the timeliness and accuracy of risk prediction and response.

[0090] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention) may not be described.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the essence and scope of the technical solution of the present invention.

Claims

1. A safety early warning method for a power trading platform based on user behavior, characterized in that: The method comprises: Step S100: Collect various transaction data of users on the platform and establish a corresponding database; Step S200: pre-processing and analyzing the collected user behavior data to obtain useful feature information; Step S300: Ensure that all transactions comply with laws and regulations and market transaction rules that have been issued by the government and those that have been optimized and rectified later, to prevent various risks and disputes; Step S400: constructing an early warning model and using a similarity analysis algorithm to calculate the similarity of user behavior based on the difference between the user's real-time behavior and the normal behavior pattern, and comparing it with the normal user behavior to determine whether there is a security risk; Step S500: Monitor user behavior in real time, use the early warning system to predict potential security risks, and take corresponding measures when necessary; Step S600: As user behavior changes and new security threats emerge, the early warning system needs to be continuously optimized and updated to ensure its accuracy and effectiveness; The step S100 includes: Step S101: Collecting user operation data, including all activities of the user on the power trading platform; Step S102: Collecting user behavior data such as the user's electricity consumption, electricity consumption time, and electricity consumption frequency, the user's electricity expenditure, consumption data of payment records, the user's real-time or predicted load data, the user's credit record, credit data of payment behavior, the user's loyalty to the power supplier, stickiness data such as usage time, and environmental protection data such as the user's energy use efficiency and environmental protection behavior; Step S103: sorting various data of users of the power information intranet and building a database of user behavior; The step S200 includes: Step S201: Data cleaning. For missing data, methods such as ignoring missing data, deleting negative values, filling with constant values, filling with overall mean values, filling with most likely values, and filling with regression methods can be used for processing. Data reduction can be performed by direct deletion, chi-square test, information gain, information gain rate, and other methods to reduce the size of the database while retaining key information. Data can be converted by methods such as decimal calibration, maximum and minimum values, and Z-SCORE to better adapt to subsequent data analysis models. Step S201: Data mining, using ID3 decision tree and analytic hierarchy process, can effectively classify and cluster the user behavior data, thereby discovering patterns and associations in the data; Step S203: Feature extraction: Extracting useful features from user behavior data is the key to analysis. These features can be used to describe user behavior patterns and interests. The step S300 includes: Step S301: Strengthen supervision. The supervision of the power trading platform should follow the principles of openness, fairness and efficiency to ensure that market members consciously abide by relevant laws and regulations. The supervision targets include power trading entities, power market operating institutions and power grid companies that provide transmission and distribution services; Step S302: Improve the supervision content, which should include the fulfillment of power system safety obligations, entry and exit from the power market, qualifications for participating in power market transactions, implementation of power market operation rules, transactions and electricity fee settlement, information disclosure, implementation of national standards and industry standards, and balance of fund management and fund use, etc., and introduce innovative supervision methods. With the deepening of the low-carbon transformation of the energy structure, the safety, reliability and real-time performance of the new power system are facing challenges. The power market supervision method should introduce innovative methods such as third-party agency business evaluation, supervision information system application and credit supervision mechanism to adapt to the new situation of power market development; Step S303: clarify the rights and responsibilities, create a fair trading guarantee mechanism, standardize the responsibilities of power market entities including power trading entities, power market operating institutions and power grid enterprises, constrain market access, exit and intervention behaviors, clarify the responsibilities of regulatory agencies and their implementation methods, prevent unfair competition in the market, and strengthen information disclosure. The operation of the power trading platform should be based on relatively independent trading centers within the management areas of the State Grid and the Southern Power Grid, lay the foundation for information disclosure of power market transactions, and ensure the transaction quality of the power market; Step S303: Strengthen network security management. In the face of network security challenges in the power industry, a network security management agency should be established in the power industry, network security infrastructure construction and awareness education should be strengthened in the power industry, a network security emergency response team should be established, risk assessment and monitoring should be carried out, and advanced network security protection technologies should be adopted to effectively ensure that all transaction businesses comply with laws and regulations, prevent various risk disputes, and thus maintain the order of the power market and the interests of participants; The step S400 includes: Step S401: Selecting a suitable modeling method, according to the characteristics of the collected and analyzed user behavior data, selecting a suitable time series modeling method to predict future electricity consumption trends; Step S402: Model training and validation, using historical data to train the model, and evaluating the accuracy and stability of the model through methods such as cross-validation; Step S403: Model deployment and monitoring: deploy the trained model on the power trading platform, monitor the difference between the user's power consumption behavior and the normal behavior pattern in real time, use the similarity analysis algorithm to calculate the similarity of user behavior, and compare it with the normal user behavior. Once abnormal behavior or potential risk is found, the early warning mechanism is triggered immediately; The step S500 includes: Step S501: Combine advanced network security technology to prevent and control risk points in the entire platform transaction process, including a complete rule system, advanced identity authentication mechanism, etc., and strengthen data security management, establish a complete data encryption mechanism, ensure the security of data during transmission and storage, and protect user data from unauthorized access and leakage by adopting advanced encryption technology, and strictly manage data access rights to prevent unauthorized access and leakage. By implementing access control strategies, ensure that only authorized users can access sensitive data, thereby reducing the risk of data leakage; Step S502: Establish a comprehensive monitoring and early warning mechanism to promptly discover and resolve potential problems and prevent system failures or crashes; Step S503: By monitoring user behavior in real time, the early warning system can predict potential security risks and take corresponding measures when necessary; The step S600 includes: Step S601: With the development of network technology and the continuous changes in attack methods, the security monitoring and early warning system needs to be continuously updated and upgraded to adapt to new security threats. At the same time, the security monitoring and early warning system is integrated with other network security tools and technologies to achieve information sharing and collaborative work, and improve the overall security protection capabilities; Step S602: Strengthen the construction and training of the security team, improve the professional skills and response capabilities of personnel, ensure the effective implementation of security monitoring and early warning work, and introduce new technologies, use big data, artificial intelligence and other technologies to conduct in-depth analysis of user behavior, and improve the accuracy and real-time performance of anomaly detection; Step S603: Adjust and optimize the model according to the warning effect, including parameter adjustment, feature selection, etc., to improve the accuracy and timeliness of the warning, and continuously monitor and update.

2. The safety warning system of the power trading platform safety warning method based on user behavior according to claim 1 is characterized in that: The system includes a user behavior information collection module, a user behavior data analysis module, a legal protection module, a database, a machine learning model construction module, a risk prediction and response module, and a continuous optimization and update module; The user behavior information collection module is used to collect various transaction data of users on the platform and establish a corresponding database; The user behavior data analysis module is used to pre-process and analyze the collected user behavior data to obtain useful feature information; The legal guarantee module is used to ensure that all transactions comply with laws and regulations and market transaction rules that have been issued by the government and those that have been optimized and rectified later, to prevent various risk disputes; The machine learning model building module is used to build an early warning model and calculate the similarity of user behavior based on the difference between the user's real-time behavior and the normal behavior pattern using a similarity analysis algorithm, and compare it with the normal user behavior to determine whether there is a security risk; The risk prediction and response module is used to monitor user behavior in real time, predict potential security risks using the early warning system, and take corresponding measures when necessary; The continuous optimization and update module needs to continuously optimize and update the early warning system as user behavior changes and new security threats emerge to ensure its accuracy and effectiveness; The database is used to provide data support and data comparison to the machine learning model construction module, the user behavior data analysis module, the risk prediction and response module, and the continuous optimization and update module, and can store and update data. The machine learning model construction module can provide data support to the risk prediction and response module to ensure the timeliness and accuracy of risk prediction and response.

Images