A transaction data encryption communication method and system

Abstract

The present invention relates to the field of data encryption and electrical communication technology, and in particular to a transaction data encryption communication method and system. The method comprises the following steps: obtaining a biometric signal of a communication user; performing signal encoding conversion on the biometric signal of the communication user to generate user biometric data; obtaining network environment information of the communication user; performing parameter feature extraction on the network environment information of the communication user to obtain communication network parameter features; performing communication protocol identification on the communication network parameter features to obtain communication protocol data. The present invention realizes the correlation analysis of communication transaction status based on the biometric rheological information and network performance status information of the transaction user through data processing technology, pattern recognition technology and data encryption technology; realizes the dynamic encryption strategy adjustment of transaction data, thereby improving the communication security of transaction data.

Description

Technical Field

[0001] The present invention relates to the technical field of data encryption and electrical communication, and in particular to a transaction data encryption communication method and system. Background Art

[0002] Due to its large volume, wide variety and complex sources, the accuracy, authenticity and security of transaction data are difficult to guarantee. Specifically, transaction data is vulnerable to unauthorized access, modification or destruction in data storage. The confidentiality of transaction data is vulnerable to threats during data transmission, including malware and hacker attacks. In addition, transaction data is dynamically changing and requires real-time processing and analysis, which increases the difficulty of protecting transaction data. With the implementation of data classification and management for transaction data and the application of data encryption technology in the field of transaction data processing, transaction data can be encrypted and communicated, which ensures the security of transaction data to a certain extent. However, the existing transaction data encryption communication technology only conducts an overall analysis of transaction data and selects a fixed data encryption algorithm. In particular, when users conduct real-time transaction communications, the communication transaction status correlation analysis is not performed based on the transaction user's biometric flow information and network performance status information. In addition, there is a lack of dynamic encryption strategy adjustment for transaction data, resulting in low communication security of transaction data. Summary of the invention

[0003] Based on this, it is necessary to provide a transaction data encryption communication method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a transaction data encryption communication method is provided, the method comprising the following steps:

[0005] Step S1: acquiring a biometric signal of a communication user; performing signal encoding conversion on the biometric signal of the communication user to generate user biometric data;

[0006] Step S2: Acquire communication user network environment information; extract parameter features of the communication user network environment information to obtain communication network parameter features; perform communication protocol identification on the communication network parameter features to obtain communication protocol data; perform communication network performance status evaluation on the communication user network environment information according to the communication protocol data to generate network performance status data;

[0007] Step S3: acquiring user transaction data; performing transaction user biometric rheological analysis on user biometric data according to the user transaction data to generate biometric rheological data; performing user communication status correlation analysis based on biometric rheological data and network performance status data to obtain user communication status analysis data; performing communication encryption strategy generation on user transaction data through user communication status analysis data to obtain communication encryption strategy data;

[0008] Step S4: construct a communication encryption policy model based on the communication encryption policy data to generate a communication encryption policy model; dynamically encrypt the user transaction data based on the communication encryption policy model to obtain dynamically encrypted transaction data; upload the dynamically encrypted transaction data to the blockchain network distributed storage to generate a transaction data encrypted communication file.

[0009] The present invention obtains the biometric signal of the communication user and converts it into user biometric data to ensure the accuracy and security of the user's personal identity authentication during the communication process; this biometric-based authentication mechanism can effectively prevent identity theft and unauthorized access, thereby enhancing the security of the communication system; by obtaining the network environment information of the communication user, extracting parameter features and identifying the communication protocol, the performance status of the communication network is accurately evaluated, and the network performance can be monitored and optimized to ensure the stability and efficiency of the communication process, while improving the reliability of data transmission; the user transaction data is subjected to the rheological analysis of the transaction user's biometric characteristics, and the user's communication status is analyzed in combination with the network performance status data The correlation analysis of the user's communication status is clarified, and then targeted communication encryption strategy data is formulated; the encryption strength is dynamically adjusted according to the user's communication status to improve the security of transaction data; the communication encryption strategy model constructed according to the communication encryption strategy data can dynamically encrypt user transaction data; this dynamic encryption method can adjust the encryption algorithm according to the real-time communication environment and user status to ensure the security of transaction data during transmission; uploading these dynamically encrypted transaction data to the blockchain network for distributed storage can further ensure the immutability and integrity of the data, and the generated transaction data encrypted communication file provides a safe and reliable recording and verification mechanism for the transaction. Therefore, the present invention realizes the correlation analysis of communication transaction status according to the biometric rheological information and network performance situation information of the transaction user through data processing technology, pattern recognition technology and data encryption technology; realizes the dynamic encryption strategy adjustment of transaction data, thereby improving the communication security of transaction data.

[0010] Preferably, step S2 comprises the following steps:

[0011] Step S21: Acquire the communication user network environment information; perform network signal marking on the communication user network environment information to generate network signal marking data;

[0012] Step S22: recording the communication network signal parameters of the communication user network environment information according to the network signal tag data to obtain the communication network signal parameters; calculating the network bandwidth of the communication network signal parameters to generate the communication network bandwidth parameters; identifying the regional parameters of the network based on the communication network signal parameters and the communication network bandwidth parameters to obtain the communication network regional parameters;

[0013] Step S23: integrating the communication network signal parameters, the communication network bandwidth parameters and the communication network area parameters to obtain the communication network parameter characteristics; extracting the network packet header information from the communication network parameter characteristics to generate the network packet header information; reading the protocol identification field from the network packet header information to obtain the protocol identification field; performing communication protocol pattern recognition on the protocol identification field to obtain the communication protocol data;

[0014] Step S24: Evaluate the communication network performance status of the communication user network environment information according to the communication protocol data to generate network performance status data.

[0015] The present invention can provide basic data for subsequent network performance analysis by acquiring network environment information of communication users and marking network signals, thereby ensuring accurate recording and analysis of network signals; recording communication network signal parameters of communication user network environment information according to network signal marking data, combining communication network bandwidth parameters generated by network bandwidth measurement, and identifying network region parameters based on these parameters, thereby jointly providing comprehensive network parameter support for the network environment, which is helpful to explain the physical characteristics and geographical location of the network; integrating communication network signal parameters, communication network bandwidth parameters and communication network region parameters to obtain communication network parameter features, extracting network packet header information and reading protocol identification fields for these parameter features, thereby clearly identifying and parsing different communication protocols in the network; evaluating the communication network performance status of communication user network environment information according to communication protocol data, thereby comprehensively reflecting the operating status and performance indicators of the network.

[0016] Preferably, step S24 includes the following steps:

[0017] Step S241: classifying the communication protocol data into communication protocol types to obtain communication protocol type data; calculating the protocol network throughput of the communication network parameter characteristics according to the communication protocol type data to generate the protocol network throughput;

[0018] Step S242: recording the network signal strength of the protocol network throughput based on the communication network signal parameter to obtain a network signal strength value; extracting the signal strength peak and valley values ​​of the network signal strength value to obtain a signal strength peak and valley value;

[0019] Step S243: performing network bandwidth flux measurement on the signal strength peak and valley values ​​according to the communication network bandwidth parameter to generate network bandwidth flux data; performing bandwidth flux change pattern recognition on the network bandwidth flux data to obtain the bandwidth flux change pattern;

[0020] Step S244: performing network area bandwidth pattern matching on the communication network area parameters through the bandwidth flux change pattern to obtain a network area bandwidth matching pattern; performing network area performance distribution mapping on the network area bandwidth matching pattern based on the signal strength peak and valley values ​​to generate a network area performance distribution map;

[0021] Step S245: performing regional network signal distribution strength identification on the network regional performance distribution map to obtain regional network signal distribution data; performing network signal stability status evaluation on the network regional performance distribution map based on the regional network signal distribution data to obtain network signal stability status data;

[0022] Step S246: measuring the regional network bandwidth load on the network regional performance distribution map to generate the regional network bandwidth load; evaluating the network bandwidth utilization status on the network regional performance distribution map based on the regional network bandwidth load to generate network bandwidth utilization status data;

[0023] Step S247: Use the network signal stability status data and the network bandwidth utilization status data to evaluate the communication network performance status of the communication user network environment information to generate network performance status data.

[0024] The present invention divides communication protocol data into communication protocol types, providing a clear classification basis for subsequent network throughput measurement; the protocol network throughput measurement based on these data can effectively reflect the data transmission capacity of different protocols in the network; the network signal strength is recorded for the protocol network throughput based on the communication network signal parameters, and the obtained network signal strength value provides basic data for signal quality evaluation; the signal strength peak and valley value extraction of the network signal strength value can reveal and identify the fluctuation of the network signal; the network bandwidth flux measurement of the signal strength peak and valley value based on the communication network bandwidth parameter can reflect the bandwidth usage of the network under different conditions; the bandwidth flux change pattern recognition of these data can grasp the trend of network performance changes; the network regional bandwidth pattern matching of the communication network regional parameters through the bandwidth flux change pattern can reveal the bandwidth of different regions. wide characteristics; mapping the network regional performance distribution of the network regional bandwidth matching mode based on the signal strength peak and valley values ​​is convenient for identifying regional differences in network performance; regional network signal distribution strength is identified on the network regional performance distribution map, which provides a basis for evaluating network coverage and signal quality; network signal stability status evaluation based on these data on the network regional performance distribution map can identify problems in the network; regional network bandwidth load is measured on the network regional performance distribution map, which provides a quantitative basis for the use of network resources; network bandwidth utilization status evaluation based on the regional network bandwidth load on the network regional performance distribution map can reflect the utilization efficiency of network resources; network signal stability status data and network bandwidth utilization status data are used to evaluate the communication network performance status of communication users' network environment information, so as to fully reflect the operation status and performance indicators of the network.

[0025] Preferably, step S3 comprises the following steps:

[0026] Step S31: Acquire user transaction data; extract user transaction behavior features from the user transaction data to obtain user transaction behavior feature data;

[0027] Step S32: performing transaction pattern recognition on the user transaction behavior feature data to generate transaction pattern data; performing transaction user biometric rheological analysis on the user biometric data according to the transaction pattern data to generate biometric rheological data;

[0028] Step S33: performing user communication status correlation analysis based on biometric rheological data and network performance status data to obtain user communication status analysis data;

[0029] Step S34: Generate a communication encryption strategy for user transaction data based on user communication status analysis data to obtain communication encryption strategy data.

[0030] The present invention obtains user transaction data and performs user transaction behavior feature extraction on it, so as to clarify the user transaction behavior feature information; performs transaction pattern recognition on the user transaction behavior feature data, which can reveal the user's transaction rules and behavior patterns; performs transaction user biometric rheological analysis on the user biometric data in combination with the transaction pattern data, which is helpful to identify the physiological and behavioral changes of the user during the transaction process, and provide biometric dimension data for transaction security and user identity authentication; the user communication status association analysis based on the biometric rheological data and the network performance status data can comprehensively evaluate the user's communication environment and personal biometric rheological state; it can identify the factors affecting transaction security, which is conducive to taking corresponding measures in the future, thereby enhancing the security of the communication process; the communication encryption strategy for the user transaction data is generated through the user communication status analysis data, which can provide customized encryption protection for the user's transaction data; this strategy can dynamically adjust the encryption strength according to the user's communication status to ensure the security of the transaction data during transmission.

[0031] Preferably, step S32 includes the following steps:

[0032] Step S321: Record the transaction amount of the user transaction behavior characteristic data to obtain transaction amount data; monitor the transaction duration of the user transaction behavior characteristic data according to the transaction amount data to obtain the transaction duration; perform transaction pattern matching based on the transaction amount data and the transaction duration to generate transaction pattern data;

[0033] Step S322: extracting user eye features from the user biometric data to obtain user eye feature information; performing eye tracking on the user eye feature information to generate user eye tracking information; recording the number of blinking features from the user eye tracking information to obtain the number of blinking features of the user;

[0034] Step S323: Calculate the user's eye movement frequency based on the number of user blinking features according to the transaction duration to generate the user's transaction eye movement frequency; monitor the user's transaction eye movement feature change frequency based on the transaction pattern data to obtain the transaction eye movement feature change frequency;

[0035] Step S324: performing hand temperature recognition on the user's biometric data to obtain the user's hand temperature data; monitoring the change of the user's transaction hand temperature on the user's hand temperature data based on the transaction mode data to obtain the transaction hand temperature change frequency;

[0036] Step S325: Merge the transaction user feature rheology by combining the transaction eye movement feature change frequency and the transaction hand temperature change frequency to generate biometric feature rheology data.

[0037] The present invention records the transaction amount of the user's transaction behavior characteristic data, and combines it with the transaction duration monitoring to ensure that the user's transaction scale and transaction duration are captured; the transaction pattern matching based on the two data can reveal the user's transaction habits and behavior patterns; the eye features in the user's biometric data are extracted, and the user's line of sight activities are recorded in detail through eye tracking technology; the number of blinking characteristics of the user's eye tracking situation is recorded to clarify the relevant situation of the user's blinking behavior; the user's eye movement frequency is calculated based on the number of blinking characteristics of the user according to the transaction duration, and combined with the transaction pattern data, the frequency of changes in the user's eye movement characteristics during the transaction process can be monitored, which is helpful to identify the user's concentration level during the transaction process; the user's hand temperature is identified for the user's biometric data, and combined with the transaction pattern data, the frequency of changes in the user's hand temperature during the transaction process is monitored; the transaction eye movement feature change frequency and the transaction hand temperature change frequency are combined for transaction user feature rheology to form biometric rheological data, which provides multi-dimensional data information for comprehensive evaluation of the user's physiological and behavioral status during the transaction process.

[0038] Preferably, step S33 includes the following steps:

[0039] Step S331: performing transaction eye movement abnormal frequency detection on the biometric rheological data to obtain transaction eye movement abnormal frequency data; performing transaction hand temperature abnormal change monitoring on the biometric rheological data to obtain transaction hand temperature abnormal data;

[0040] Step S332: performing real-time signal fluctuation tracking on the network performance status data to generate signal fluctuation tracking data; performing signal abnormal jitter state identification on the network signal stability status data according to the signal fluctuation tracking data to obtain signal abnormal jitter data;

[0041] Step S333: performing real-time monitoring of network bandwidth utilization on the network performance status data to generate bandwidth utilization monitoring data; performing low bandwidth utilization detection on the network bandwidth utilization status data through the bandwidth utilization monitoring data to obtain low bandwidth utilization data;

[0042] Step S334: performing an eye movement signal jitter anomaly correlation on the transaction eye movement abnormal frequency data and the signal abnormal jitter data to obtain eye movement-signal correlation data; performing an eye movement bandwidth efficiency anomaly correlation on the transaction eye movement abnormal frequency data and the bandwidth low utilization rate data to obtain eye movement-bandwidth correlation data;

[0043] Step S335: Correlate the transaction hand temperature abnormality data and the signal abnormal jitter data with the hand temperature signal jitter abnormality to obtain hand temperature-signal correlation data; correlate the transaction hand temperature abnormality data and the bandwidth low utilization rate data with the hand temperature bandwidth efficiency abnormality to obtain hand temperature-bandwidth correlation data;

[0044] Step S336: Associating and merging the eye movement-signal correlation data, the eye movement-bandwidth correlation data, the hand temperature-signal correlation data, and the hand temperature-bandwidth correlation data with the user communication status to obtain user communication status analysis data.

[0045] The present invention detects abnormal transaction eye movement frequency on biometric rheological data, so as to reveal abnormal patterns of eye activities of users during transactions; at the same time, monitors abnormal changes in transaction hand temperature, and can identify abnormal changes in user hand temperature during transactions; these data help to timely discover physiological or behavioral abnormalities of users, thereby enhancing the security of transactions; real-time tracking of signal fluctuations on network performance status data can reflect the stability of network signals in real time; identification of abnormal signal jitter states based on these data helps to identify abnormal fluctuations in network signals; real-time monitoring of network bandwidth utilization on network performance status data can reflect the use of network bandwidth in real time; low bandwidth utilization detection based on these data helps to identify inefficient use of network bandwidth; eye movement signal jitter is performed on eye movement abnormal frequency data and signal abnormal jitter data Abnormal correlation can reveal the correlation between user eye activity and network signal stability; at the same time, correlating the transaction eye movement abnormal frequency data and bandwidth low utilization data with eye movement bandwidth efficiency abnormality can help understand the relationship between user eye activity and network bandwidth utilization efficiency; correlating the transaction hand temperature abnormal data and signal abnormal jitter data with hand temperature signal jitter abnormality can reveal the correlation between user hand temperature and network signal stability; at the same time, correlating the transaction hand temperature abnormal data and bandwidth low utilization data with hand temperature bandwidth efficiency abnormality can clarify the relationship between user hand temperature and network bandwidth utilization efficiency; correlating the eye movement-signal correlation data, eye movement-bandwidth correlation data, hand temperature-signal correlation data and hand temperature-bandwidth correlation data with user communication status can comprehensively reflect the relationship between the user's physiological and behavioral state during the transaction process and network efficiency.

[0046] Preferably, step S34 includes the following steps:

[0047] Step S341: Generate a preliminary transaction data key for the user transaction data according to the transaction mode data to obtain a preliminary transaction data key;

[0048] Step S342: increasing the overall length of the key for the preliminary transaction data key by using the eye movement-signal association data to obtain measures for increasing the overall length of the key; adjusting the key encryption parameters of the preliminary transaction data key by using the eye movement-bandwidth association data to obtain measures for adjusting the key encryption parameters;

[0049] Step S343: increasing the number of key verification bits for the preliminary transaction data key based on the hand temperature-signal correlation data, and generating a key verification bit increase measure; adjusting the number of key structure layers for the preliminary transaction data key based on the hand temperature-bandwidth correlation data, and generating a key encryption layer adjustment measure;

[0050] Step S344: Integrate the measures for increasing the overall length of the key, adjusting the key encryption parameters, increasing the number of key verification bits, and adjusting the number of key encryption layers to obtain communication encryption policy data.

[0051] The present invention generates a preliminary key for transaction data based on transaction mode data of user transaction data, provides a preliminary key basis for transaction data, and ensures that the transaction data has a certain degree of security in the preliminary stage; increases the overall length of the preliminary key for transaction data through eye movement-signal association data, and the implemented measures for increasing the overall length of the key can increase the complexity of the key, thereby enhancing the security of encryption; at the same time, adjusts the key encryption parameters of the preliminary key for transaction data through eye movement-bandwidth association data, and the implemented key encryption parameter adjustment measures can optimize the encryption algorithm according to the physiological and behavioral characteristics of the user, further improving the encryption efficiency and security; adjusts the preliminary key for transaction data based on hand temperature-signal association data The key increases the number of key verification bits, and the generated key verification bit increase measures can enhance the key verification mechanism, making the key more difficult to crack; in addition, the key structure layers of the preliminary key of the transaction data are adjusted based on the hand temperature-bandwidth correlation data, and the generated key encryption layer adjustment measures can improve the data protection level by increasing the number of encryption layers, ensuring the security of the data during transmission; integrating the overall key length increase measures, key encryption parameter adjustment measures, key verification bit increase measures and key encryption layer adjustment measures to obtain communication encryption strategy data; providing a comprehensive and multi-level encryption scheme for transaction data, ensuring the confidentiality and integrity of transaction data in different network environments.

[0052] Preferably, step S4 comprises the following steps:

[0053] Step S41: constructing a communication encryption strategy model according to the communication encryption strategy data to obtain a communication encryption strategy pre-model; and using the communication encryption strategy data to perform model training on the communication encryption strategy pre-model to obtain a communication encryption strategy training model;

[0054] Step S42: performing model cross-validation evaluation on the communication encryption strategy training model to obtain model evaluation data; adjusting model parameters of the communication encryption strategy training model using the model evaluation data to obtain a communication encryption strategy model;

[0055] Step S43: dynamically encrypting the user transaction data based on the communication encryption strategy model to obtain dynamically encrypted transaction data;

[0056] Step S44: Use blockchain technology to upload the dynamically encrypted transaction data to the blockchain node, and distribute the dynamically encrypted transaction data through the blockchain node to generate a transaction data encrypted communication file.

[0057] The present invention constructs a communication encryption strategy model according to communication encryption strategy data to obtain a communication encryption strategy pre-model, which provides a preliminary strategy framework for the encryption process; uses the communication encryption strategy data to perform model training on the communication encryption strategy pre-model, which can optimize the encryption algorithm according to actual data, improve encryption efficiency and adaptability, and ensure the effective implementation of the encryption strategy; performs model cross-validation evaluation on the communication encryption strategy training model, which can provide quantitative feedback on model performance; adjusts model parameters of the communication encryption strategy training model through model evaluation data, which can be further optimized to improve encryption accuracy and efficiency, and ensure the reliability and stability of the model in practical applications; based on communication encryption strategy data, the communication encryption strategy training model can be used for model training, and the communication encryption strategy pre-model ... The trust encryption strategy model dynamically encrypts user transaction data and can adjust the encryption strength according to the real-time communication environment and user behavior characteristics; this dynamic encryption method improves the security of data transmission, reduces the risk of data being intercepted and cracked during transmission, and ensures the security and privacy of transaction data; blockchain technology is used to upload dynamically encrypted transaction data to blockchain nodes, and the dynamically encrypted transaction data is distributedly stored through blockchain nodes, which can ensure the immutability and transparency of the data; this distributed storage mechanism enhances the persistence and reliability of data, while providing a secure and traceable transaction record, providing a solid foundation for transaction verification and auditing.

[0058] Preferably, step S43 includes the following steps:

[0059] Step S431: performing real-time communication transaction status monitoring on user transaction data based on user communication status analysis data to obtain communication transaction status monitoring data;

[0060] Step S432: extracting eye movement signal jitter anomalies from the communication transaction status monitoring data to obtain eye movement signal jitter anomaly monitoring conditions; identifying eye movement bandwidth efficiency anomalies from the communication transaction status monitoring data to generate eye movement bandwidth efficiency anomaly monitoring conditions;

[0061] Step S433: extract hand temperature signal jitter anomaly from the communication transaction status monitoring data to obtain hand temperature signal jitter anomaly monitoring status; identify hand temperature bandwidth efficiency anomaly from the communication transaction status monitoring data to generate hand temperature bandwidth efficiency anomaly monitoring status;

[0062] Step S434: Based on the communication encryption strategy model, a key overall length increase measure is executed for the abnormal monitoring of eye movement signal jitter to generate key length execution data; based on the communication encryption strategy model, a key encryption parameter adjustment measure is executed for the abnormal monitoring of eye movement bandwidth efficiency to obtain encryption parameter adjustment execution data;

[0063] Step S435: Based on the communication encryption strategy model, a key verification bit increase measure is executed for the abnormal monitoring of hand temperature signal jitter, and verification bit execution data is generated; based on the communication encryption strategy model, a key encryption layer number adjustment measure is executed for the abnormal monitoring of hand temperature bandwidth efficiency, and encryption layer number adjustment execution data is obtained;

[0064] Step S436: The key length execution data, encryption parameter adjustment execution data, verification bit number execution data and encryption layer number adjustment execution data are dynamically encrypted and combined to obtain dynamically encrypted transaction data.

[0065] The present invention performs real-time communication transaction status monitoring on user transaction data based on user communication status analysis data, which can reflect the communication status in the transaction process in real time, so as to timely discover abnormal situations in the transaction process and provide real-time monitoring for transaction security; the communication transaction status monitoring data is subjected to eye movement signal jitter anomaly extraction, which can identify abnormal patterns of user eye activities; at the same time, the communication transaction status monitoring data is subjected to eye movement bandwidth efficiency anomaly identification, which reveals the abnormal relationship between user eye movement activities and network bandwidth utilization, and provides multi-dimensional analysis data for transaction security monitoring; the communication transaction status monitoring data is subjected to hand temperature signal jitter anomaly extraction, which can identify abnormal changes in user hand temperature; at the same time, the communication transaction status monitoring data is subjected to hand temperature bandwidth efficiency anomaly identification, which reveals the abnormal relationship between user hand temperature and network bandwidth utilization, and provides further physiological characteristic data for transaction security monitoring; the communication transaction status monitoring data is subjected to eye movement signal jitter anomaly extraction, which can identify abnormal changes in user hand temperature; at the same time, the communication transaction status monitoring data is subjected to hand temperature bandwidth efficiency anomaly identification, which reveals the abnormal relationship between user hand temperature and network bandwidth utilization, and provides further physiological characteristic data for transaction security monitoring; the communication encryption strategy model is used to identify eye movement signal jitter anomaly By increasing the overall key length in response to regular monitoring conditions, the complexity of the encryption key can be enhanced. At the same time, by adjusting the key encryption parameters in response to abnormal eye movement bandwidth efficiency monitoring based on the communication encryption strategy model, the encryption algorithm can be optimized to improve the security and efficiency of encryption. By increasing the number of key verification bits in response to abnormal hand temperature signal jitter monitoring based on the communication encryption strategy model, the key verification mechanism can be enhanced. At the same time, by adjusting the number of key encryption layers in response to abnormal hand temperature bandwidth efficiency monitoring based on the communication encryption strategy model, the encryption level can be increased to improve the data protection level. By merging the key length execution data, encryption parameter adjustment execution data, verification bit execution data and encryption layer adjustment execution data through dynamic encryption execution, the encryption strategy can be dynamically adjusted according to real-time monitoring results. This ensures that transaction data can be properly protected under different circumstances, thereby enhancing the security and adaptability of transaction data.

[0066] In this specification, a transaction data encryption communication system is provided, which is used to execute the transaction data encryption communication method mentioned above, and the transaction data encryption communication system includes:

[0067] A user biometric feature acquisition module is used to obtain a communication user's biometric feature signal; perform signal encoding conversion on the communication user's biometric feature signal to generate user biometric feature data;

[0068] The communication network performance status evaluation module is used to obtain the communication user network environment information; extract the parameter characteristics of the communication user network environment information to obtain the communication network parameter characteristics; perform communication protocol identification on the communication network parameter characteristics to obtain communication protocol data; perform communication network performance status evaluation on the communication user network environment information according to the communication protocol data to generate network performance status data;

[0069] The communication encryption strategy generation module is used to obtain user transaction data; perform transaction user biometric rheological analysis on user biometric data according to user transaction data to generate biometric rheological data; perform user communication status correlation analysis based on biometric rheological data and network performance status data to obtain user communication status analysis data; generate communication encryption strategy for user transaction data through user communication status analysis data to obtain communication encryption strategy data;

[0070] The dynamic encryption execution module is used to build a communication encryption policy model based on the communication encryption policy data and generate a communication encryption policy model; dynamically encrypt user transaction data based on the communication encryption policy model to obtain dynamically encrypted transaction data; upload the dynamically encrypted transaction data to the blockchain network distributed storage to generate a transaction data encrypted communication file.

[0071] The present invention obtains the biometric signal of the communication user through the user biometric acquisition module and converts it into user biometric data, ensuring the accuracy and security of the user's personal identity authentication during the communication process; this biometric-based authentication mechanism can effectively prevent identity theft and unauthorized access, thereby enhancing the security of the communication system; the communication network performance status evaluation module obtains the network environment information of the communication user, and performs parameter feature extraction and communication protocol identification on it, accurately evaluates the performance status of the communication network, and can monitor and optimize network performance, ensure the stability and efficiency of the communication process, and improve the reliability of data transmission; the communication encryption strategy generation module performs transaction user biometric rheological analysis on user transaction data, combined with network performance Situation data is used to perform correlation analysis of user communication status, clarify the user communication status, and then formulate targeted communication encryption strategy data; the encryption strength is dynamically adjusted according to the user's communication status to improve the security of transaction data; the communication encryption strategy model constructed by the dynamic encryption execution module according to the communication encryption strategy data can dynamically encrypt user transaction data; this dynamic encryption method can adjust the encryption algorithm according to the real-time communication environment and user status to ensure the security of transaction data during transmission; uploading these dynamically encrypted transaction data to the blockchain network for distributed storage can further ensure the immutability and integrity of the data, and the generated transaction data encrypted communication file provides a safe and reliable recording and verification mechanism for the transaction. Therefore, the present invention realizes the correlation analysis of communication transaction status according to the biometric rheological information and network performance situation information of the transaction user through data processing technology, pattern recognition technology and data encryption technology; realizes the dynamic encryption strategy adjustment of transaction data, thereby improving the communication security of transaction data. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A schematic diagram of the steps of a transaction data encryption communication method;

[0073] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0074] Figure 3 for Figure 2 Detailed implementation steps of step S32;

[0075] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0076] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0077] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0078] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0079] To achieve this, please refer to Figures 1 to 3 , a transaction data encryption communication method, the method comprising the following steps:

[0080] Step S1: acquiring a biometric signal of a communication user; performing signal encoding conversion on the biometric signal of the communication user to generate user biometric data;

[0081] Step S2: Acquire communication user network environment information; extract parameter features of the communication user network environment information to obtain communication network parameter features; perform communication protocol identification on the communication network parameter features to obtain communication protocol data; perform communication network performance status evaluation on the communication user network environment information according to the communication protocol data to generate network performance status data;

[0082] Step S3: acquiring user transaction data; performing transaction user biometric rheological analysis on user biometric data according to the user transaction data to generate biometric rheological data; performing user communication status correlation analysis based on biometric rheological data and network performance status data to obtain user communication status analysis data; performing communication encryption strategy generation on user transaction data through user communication status analysis data to obtain communication encryption strategy data;

[0083] Step S4: construct a communication encryption policy model based on the communication encryption policy data to generate a communication encryption policy model; dynamically encrypt the user transaction data based on the communication encryption policy model to obtain dynamically encrypted transaction data; upload the dynamically encrypted transaction data to the blockchain network distributed storage to generate a transaction data encrypted communication file.

[0084] The present invention obtains the biometric signal of the communication user and converts it into user biometric data to ensure the accuracy and security of the user's personal identity authentication during the communication process; this biometric-based authentication mechanism can effectively prevent identity theft and unauthorized access, thereby enhancing the security of the communication system; by obtaining the network environment information of the communication user, extracting parameter features and identifying the communication protocol, the performance status of the communication network is accurately evaluated, and the network performance can be monitored and optimized to ensure the stability and efficiency of the communication process, while improving the reliability of data transmission; the user transaction data is subjected to the rheological analysis of the transaction user's biometric characteristics, and the user's communication status is analyzed in combination with the network performance status data The correlation analysis of the user's communication status is clarified, and then targeted communication encryption strategy data is formulated; the encryption strength is dynamically adjusted according to the user's communication status to improve the security of transaction data; the communication encryption strategy model constructed according to the communication encryption strategy data can dynamically encrypt user transaction data; this dynamic encryption method can adjust the encryption algorithm according to the real-time communication environment and user status to ensure the security of transaction data during transmission; uploading these dynamically encrypted transaction data to the blockchain network for distributed storage can further ensure the immutability and integrity of the data, and the generated transaction data encrypted communication file provides a safe and reliable recording and verification mechanism for the transaction. Therefore, the present invention realizes the correlation analysis of communication transaction status according to the biometric rheological information and network performance situation information of the transaction user through data processing technology, pattern recognition technology and data encryption technology; realizes the dynamic encryption strategy adjustment of transaction data, thereby improving the communication security of transaction data.

[0085] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart of the steps of a transaction data encryption communication method of the present invention. In this example, the transaction data encryption communication method includes the following steps:

[0086] Step S1: acquiring a biometric signal of a communication user; performing signal encoding conversion on the biometric signal of the communication user to generate user biometric data;

[0087] In the embodiment of the present invention, a wearable device is used; for example, a skin temperature sensor is used to obtain characteristic signals such as the user's skin temperature and hand temperature; a pulse sensor and a blood pressure sensor are used to obtain heart rate signals and blood pressure signals respectively; a tracking camera is used to continuously monitor characteristic signals such as the user's eye contour area and eyes; these data are merged to obtain a biometric signal of the communication user; Fourier transform technology is used to perform an inverse Fourier transform on the collected discrete biometric signal as a Fourier series to obtain a time domain signal; the time domain signal is converted into a frequency domain signal; further, these frequency domain signals are converted back to the time domain by an inverse Fourier transform to obtain a continuous digital signal; finally, these continuous digital signals are matched with the user's biological type to generate user biometric data, such as user skin temperature, hand temperature characteristic data, and user eye contour area, eye and other characteristic data. All user data collected by the present invention, such as user biometric data, user network environment information, and user transaction data, are collected, used, and processed under the premise of user consent, and in compliance with relevant national and regional laws and regulations.

[0088] Step S2: Acquire communication user network environment information; extract parameter features of the communication user network environment information to obtain communication network parameter features; perform communication protocol identification on the communication network parameter features to obtain communication protocol data; perform communication network performance status evaluation on the communication user network environment information according to the communication protocol data to generate network performance status data;

[0089] In an embodiment of the present invention, radio frequency identification technology (RFID) is used in combination with network data packet detection software to collect user network environment information; for example, network parameters such as data packet size, transmission rate and delay are collected through sensors deployed in the network; feature engineering technology in machine learning, such as feature extraction algorithm, is used to extract parameter features of the collected network environment information; feature extraction is specifically performed on the communication user network environment information, and non-numerical data is converted into numerical values; protocol identification technology based on traffic characteristics is used to specifically analyze the data packet header information in the network traffic and extract feature information that identifies the network protocol; for example, by analyzing the port number, flag bit and other features of the data packet, the communication protocol is identified; based on the communication protocol data and combined with the communication network indicator value, various indicators that affect the network performance are scientifically analyzed; for example, a network performance status evaluation system including five types of indicators such as network operation and maintenance, inspection, service, security and equipment maintenance is analyzed to obtain network performance status data.

[0090] Step S3: acquiring user transaction data; performing transaction user biometric rheological analysis on user biometric data according to the user transaction data to generate biometric rheological data; performing user communication status correlation analysis based on biometric rheological data and network performance status data to obtain user communication status analysis data; performing communication encryption strategy generation on user transaction data through user communication status analysis data to obtain communication encryption strategy data;

[0091] In an embodiment of the present invention, SQL query technology is used to obtain user transaction information data from a financial database, which includes information such as transaction time, transaction amount and transaction type; then, mapping analysis technology is used to perform transaction user biometric rheological analysis on user biometric data according to user transaction data, specifically mapping and analyzing the user's eye change characteristics with the transaction amount information; corresponding analysis is performed on the user's hand temperature change characteristics with the transaction time information; random forest algorithm is used to identify the communication protocol of network request characteristics in the transaction data; Apriori algorithm is used to perform comprehensive correlation analysis on biometric rheological data and network performance status data, thereby obtaining user communication status analysis data; finally, based on the user communication status analysis data, encryption algorithm technology is used to generate communication encryption strategy for user transaction data to obtain communication encryption strategy data.

[0092] Step S4: construct a communication encryption policy model based on the communication encryption policy data to generate a communication encryption policy model; dynamically encrypt the user transaction data based on the communication encryption policy model to obtain dynamically encrypted transaction data; upload the dynamically encrypted transaction data to the blockchain network distributed storage to generate a transaction data encrypted communication file.

[0093] In the embodiment of the present invention, a machine learning framework (such as TensorFlow or PyTorch) is used to build a communication encryption strategy model, and a neural network is used as the basic architecture of the model; specifically, a neural network containing 3 hidden layers is constructed, each hidden layer contains 64 neurons, and ReLU is used as the activation function; the input layer is the feature vector of the communication encryption strategy data, and the output layer is the encryption strategy result; the training model is evaluated using K-fold cross validation (such as K=5), and the average accuracy and loss value are calculated; according to the evaluation results, the number of hidden layers or the number of neurons of the model is adjusted to optimize the model structure; the feature vector of the user transaction data is input into the model, and the model Output specific encryption strategy parameters, such as key length, encryption algorithm, etc.; then use AES-256 encryption algorithm to encrypt transaction data to obtain dynamically encrypted transaction data; use blockchain technology to upload dynamically encrypted transaction data to the blockchain network for distributed storage; specifically during the upload process, each encrypted transaction data will be packaged into a block, then verified by nodes in the network, and finally added to the blockchain; encapsulate the dynamically encrypted transaction data and related encryption information into a transaction data encrypted communication file, which will contain all necessary information; specifically including the encrypted data and metadata, and store them in a standard file format.

[0094] Preferably, step S2 comprises the following steps:

[0095] Step S21: Acquire the communication user network environment information; perform network signal marking on the communication user network environment information to generate network signal marking data;

[0096] Step S22: recording the communication network signal parameters of the communication user network environment information according to the network signal tag data to obtain the communication network signal parameters; calculating the network bandwidth of the communication network signal parameters to generate the communication network bandwidth parameters; identifying the regional parameters of the network based on the communication network signal parameters and the communication network bandwidth parameters to obtain the communication network regional parameters;

[0097] Step S23: integrating the communication network signal parameters, the communication network bandwidth parameters and the communication network area parameters to obtain the communication network parameter characteristics; extracting the network packet header information from the communication network parameter characteristics to generate the network packet header information; reading the protocol identification field from the network packet header information to obtain the protocol identification field; performing communication protocol pattern recognition on the protocol identification field to obtain the communication protocol data;

[0098] Step S24: Evaluate the communication network performance status of the communication user network environment information according to the communication protocol data to generate network performance status data.

[0099] In the embodiment of the present invention, a network scanning tool (such as Nmap) and a network monitoring system (such as Wireshark) are used to collect the user's network environment information, including IP address, subnet mask, gateway and DNS server, etc.; a signal processing technology is used to mark the network signal, and a spectrum analysis tool is used to identify and mark the network signal; a network performance analysis tool is used to record the communication network signal parameters, including recording the transmission rate, delay, packet loss rate and other parameters in the network; a bandwidth testing tool (such as iPerf) is used to measure the network bandwidth; a geographic location identification technology (such as IP Geolocation API) is used to identify the regional parameters of the network; a data fusion technology is used to integrate the signal parameters, bandwidth parameters and regional parameters into communication network parameter characteristics; TCP or UDP protocol packets are filtered out in Wireshark to extract packet header information; in the network packet header information, a specific application layer protocol is identified according to the TCP or UDP port number; by analyzing the specific pattern in the protocol identification field, such as HTTP GET / POST request, the communication protocol mode is identified; the communication network performance status of the communication user network environment information is evaluated according to the communication protocol data to generate network performance status data.

[0100] Preferably, step S24 includes the following steps:

[0101] Step S241: classifying the communication protocol data into communication protocol types to obtain communication protocol type data; calculating the protocol network throughput of the communication network parameter characteristics according to the communication protocol type data to generate the protocol network throughput;

[0102] Step S242: recording the network signal strength of the protocol network throughput based on the communication network signal parameter to obtain a network signal strength value; extracting the signal strength peak and valley values ​​of the network signal strength value to obtain a signal strength peak and valley value;

[0103] Step S243: performing network bandwidth flux measurement on the signal strength peak and valley values ​​according to the communication network bandwidth parameter to generate network bandwidth flux data; performing bandwidth flux change pattern recognition on the network bandwidth flux data to obtain the bandwidth flux change pattern;

[0104] Step S244: performing network area bandwidth pattern matching on the communication network area parameters through the bandwidth flux change pattern to obtain a network area bandwidth matching pattern; performing network area performance distribution mapping on the network area bandwidth matching pattern based on the signal strength peak and valley values ​​to generate a network area performance distribution map;

[0105] Step S245: performing regional network signal distribution strength identification on the network regional performance distribution map to obtain regional network signal distribution data; performing network signal stability status evaluation on the network regional performance distribution map based on the regional network signal distribution data to obtain network signal stability status data;

[0106] Step S246: measuring the regional network bandwidth load on the network regional performance distribution map to generate the regional network bandwidth load; evaluating the network bandwidth utilization status on the network regional performance distribution map based on the regional network bandwidth load to generate network bandwidth utilization status data;

[0107] Step S247: Use the network signal stability status data and the network bandwidth utilization status data to evaluate the communication network performance status of the communication user network environment information to generate network performance status data.

[0108] In an embodiment of the present invention, a network protocol analysis tool (such as Wireshark) is used to analyze the captured network traffic to identify the protocol type in the data packet, such as TCP, UDP, and HTTP; for example, a filter is set to capture specific traffic, thereby obtaining corresponding communication protocol type data; a network performance test tool (such as iperf3) is used to measure the network throughput of a specific protocol per unit time (set to 10 minutes); a wireless signal strength analysis tool (such as inSSIDer) is used to record the signal strength of the wireless network; inSSIDer is specifically used to scan the wireless network and record the signal strength values ​​at different locations; data analysis software (such as Python with NumPy and Pandas libraries) is used to process the signal strength data and extract the peak and valley values; specifically, the signal strength data is imported into the Python environment, and the maximum and minimum values ​​of the signal strength are found using a statistical analysis method; the throughput data obtained by iperf3 is compared and analyzed with the peak and valley values ​​of the signal strength, and the network bandwidth flux data under different signal strengths is measured, and the specific calculation formula is as follows;

[0109]

[0110] in, Indicates the throughput data measured by iperf3, in Mbps; Indicates the maximum value of signal strength; Indicates the minimum value of signal strength; The network bandwidth flux data under a specific signal strength S is expressed in Mbps. The bandwidth flux data is input as a time series into the pattern recognition algorithm to analyze and identify the changing trend and pattern of the bandwidth flux. The regional bandwidth analysis tool is used to match the network bandwidth patterns in different regions in combination with the bandwidth flux change pattern. The geographic information system (GIS) software is used to map the regional network performance distribution in combination with the signal strength peak and valley values ​​and the network regional bandwidth matching pattern. The regional network performance distribution map is analyzed by GIS software to extract the network signal strength data of each region. The stability of the network signal is evaluated according to the variation range and consistency of the regional network signal distribution data. The network bandwidth usage data is collected by the Nagios monitoring tool, and the network bandwidth load of each region is analyzed. The regional network bandwidth load data is compared with the total bandwidth capacity to evaluate the utilization of the network bandwidth. The network signal stability status data and the network bandwidth utilization status data are integrated, and the performance evaluation model is used to evaluate the communication network performance status.

[0111] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes:

[0112] Step S31: Acquire user transaction data; extract user transaction behavior features from the user transaction data to obtain user transaction behavior feature data;

[0113] Step S32: performing transaction pattern recognition on the user transaction behavior feature data to generate transaction pattern data; performing transaction user biometric rheological analysis on the user biometric data according to the transaction pattern data to generate biometric rheological data;

[0114] Step S33: performing user communication status correlation analysis based on biometric rheological data and network performance status data to obtain user communication status analysis data;

[0115] Step S34: Generate a communication encryption strategy for user transaction data based on user communication status analysis data to obtain communication encryption strategy data.

[0116] In the embodiment of the present invention, a database query technology, such as SQL query statement, is used to query and extract user transaction data from a financial database, which includes information such as transaction time, transaction amount and transaction type; a feature extraction technology, such as cluster analysis technology, is used to group user transaction data using K-means clustering algorithm, and extract user transaction behavior, and specific transaction behavior includes information such as historical transaction duration and historical transaction amount; classification and pattern recognition technology in machine learning is applied to perform transaction pattern recognition on user transaction behavior feature data; time series analysis technology is used to analyze the changes of biometrics with transaction patterns in combination with user transaction patterns and biometric data; statistical analysis and correlation analysis technology, such as Pearson correlation coefficient, are used to conduct mutual comprehensive correlation analysis between biometric rheological data and network performance status data; for example, the correlation between eye movement changes and network delays of users in specific transaction modes is comprehensively analyzed to identify changes in communication status; dynamic encryption strategy generation technology, such as role-based access control (RBAC), is used according to user communication status analysis data; encryption keys and algorithms are dynamically adjusted according to user communication status analysis data, for example, a higher strength encryption algorithm is used when the network status is poor.

[0117] As an example of the present invention, refer to Figure 3 As shown, in this example, step S32 includes:

[0118] Step S321: Record the transaction amount of the user transaction behavior characteristic data to obtain transaction amount data; monitor the transaction duration of the user transaction behavior characteristic data according to the transaction amount data to obtain the transaction duration; perform transaction pattern matching based on the transaction amount data and the transaction duration to generate transaction pattern data;

[0119] Step S322: extracting user eye features from the user biometric data to obtain user eye feature information; performing eye tracking on the user eye feature information to generate user eye tracking information; recording the number of blinking features from the user eye tracking information to obtain the number of blinking features of the user;

[0120] Step S323: Calculate the user's eye movement frequency based on the number of user blinking features according to the transaction duration to generate the user's transaction eye movement frequency; monitor the user's transaction eye movement feature change frequency based on the transaction pattern data to obtain the transaction eye movement feature change frequency;

[0121] Step S324: performing hand temperature recognition on the user's biometric data to obtain the user's hand temperature data; monitoring the change of the user's transaction hand temperature on the user's hand temperature data based on the transaction mode data to obtain the transaction hand temperature change frequency;

[0122] Step S325: Merge the transaction user feature rheology by combining the transaction eye movement feature change frequency and the transaction hand temperature change frequency to generate biometric feature rheology data.

[0123] In the embodiment of the present invention, the transaction amount data of the user is obtained through SQL query statements; the transaction duration is calculated using timestamp records and data analysis tools (such as Python's Pandas library), specifically by calculating the start and end time of each transaction to form transaction duration data; clustering algorithms in machine learning (such as K-means) are applied to perform pattern recognition on the transaction amount and duration, specifically inputting the transaction amount and duration as features into the K-means algorithm to identify different transaction patterns; the user's eye image is captured by a camera, and the user's eye image is analyzed using computer vision technology (such as OpenCV) to extract eye features; eye tracking technology (such as Tobii or EyeLink) is used to monitor the user's eye trajectory, specifically using an eye tracking device to record the user's gaze point and gaze on a specific transaction interface. time, generate user eye movement tracking data; by setting the blinking time threshold (such as eye closure time less than 200 milliseconds), count the number of times the user blinks during the transaction process to form the user's blink feature number data; divide the user's blink feature number by the transaction duration to obtain the user's eye movement frequency during the transaction; combine time series analysis technology to monitor the changes in eye movement frequency, and specifically analyze the changes in the user's eye movement frequency under different transaction modes to form transaction eye movement feature change frequency data; use infrared sensors to monitor the user's hand temperature in real time, record temperature data, and form a user hand temperature data set; analyze the changes in the user's hand temperature under different transaction modes, record the change frequency, and form transaction hand temperature change frequency data; use data fusion technology to rheologically merge the transaction eye movement feature change frequency and the transaction hand temperature change frequency into transaction user feature data to form biometric feature data.

[0124] Preferably, step S33 includes the following steps:

[0125] Step S331: performing transaction eye movement abnormal frequency detection on the biometric rheological data to obtain transaction eye movement abnormal frequency data; performing transaction hand temperature abnormal change monitoring on the biometric rheological data to obtain transaction hand temperature abnormal data;

[0126] Step S332: performing real-time signal fluctuation tracking on the network performance status data to generate signal fluctuation tracking data; performing signal abnormal jitter state identification on the network signal stability status data according to the signal fluctuation tracking data to obtain signal abnormal jitter data;

[0127] Step S333: performing real-time monitoring of network bandwidth utilization on the network performance status data to generate bandwidth utilization monitoring data; performing low bandwidth utilization detection on the network bandwidth utilization status data through the bandwidth utilization monitoring data to obtain low bandwidth utilization data;

[0128] Step S334: performing an eye movement signal jitter anomaly correlation on the transaction eye movement abnormal frequency data and the signal abnormal jitter data to obtain eye movement-signal correlation data; performing an eye movement bandwidth efficiency anomaly correlation on the transaction eye movement abnormal frequency data and the bandwidth low utilization rate data to obtain eye movement-bandwidth correlation data;

[0129] Step S335: Correlate the transaction hand temperature abnormality data and the signal abnormal jitter data with the hand temperature signal jitter abnormality to obtain hand temperature-signal correlation data; correlate the transaction hand temperature abnormality data and the bandwidth low utilization rate data with the hand temperature bandwidth efficiency abnormality to obtain hand temperature-bandwidth correlation data;

[0130] Step S336: Associating and merging the eye movement-signal correlation data, the eye movement-bandwidth correlation data, the hand temperature-signal correlation data, and the hand temperature-bandwidth correlation data with the user communication status to obtain user communication status analysis data.

[0131] In the embodiment of the present invention, an abnormal detection technology based on CNN (convolutional neural network) is used to identify abnormal eye movement frequency by analyzing the pattern in the eye tracking data; specifically, the CNN model is used to train the user's eye movement data to identify the eye movement frequency range during normal transactions, and the eye movement frequency in the actual transaction is compared with this range to identify the abnormal frequency; the infrared temperature sensor monitoring technology is used to monitor the temperature changes of the user's hands during the transaction in real time; specifically, when the user performs a transaction operation, the infrared sensor is used to record the hand temperature, and the abnormal temperature change is identified by setting the temperature threshold (the temperature threshold is set to 36 degrees Celsius); a high-speed oscilloscope and jitter analysis software are used to monitor the network signal in real time, record the periodic jitter of the signal, and generate signal fluctuation tracking data; by analyzing the periodic jitter and random jitter in the signal fluctuation tracking data, the abnormal jitter state of the signal is identified; a PRTG network monitor is deployed to monitor the traffic and bandwidth usage in the network and generate bandwidth utilization monitoring data; The threshold of bandwidth utilization is set to 40% of the bandwidth flux. Combined with the bandwidth monitoring data, when the actual utilization rate is lower than the threshold, the bandwidth low utilization data is generated; through the statistical analysis method, the eye movement abnormal frequency data and the signal abnormal jitter data are correlated with the eye movement signal jitter abnormality to form the eye movement-signal correlation data; the eye movement abnormal frequency data and the bandwidth low utilization data are correlated with the eye movement bandwidth efficiency abnormality to specifically identify the situation where the bandwidth utilization rate is low when the eye movement abnormal frequency is high, and form the eye movement-bandwidth correlation data; by analyzing the transaction hand temperature abnormality data and the signal abnormal jitter data, the signal jitter pattern when the temperature is abnormal is found to form the hand temperature-signal correlation data; the transaction hand temperature abnormal data and the bandwidth low utilization data are correlated with the hand temperature bandwidth efficiency abnormality to identify the situation where the bandwidth utilization rate is low when the hand temperature is abnormal to form the hand temperature-bandwidth correlation data; through the data fusion technology, the eye movement-signal correlation data, the eye movement-bandwidth correlation data, the hand temperature-signal correlation data and the hand temperature-bandwidth correlation data are integrated to form the analysis data reflecting the user's communication status.

[0132] Preferably, step S34 includes the following steps:

[0133] Step S341: Generate a preliminary transaction data key for the user transaction data according to the transaction mode data to obtain a preliminary transaction data key;

[0134] Step S342: increasing the overall length of the key for the preliminary transaction data key by using the eye movement-signal association data to obtain measures for increasing the overall length of the key; adjusting the key encryption parameters of the preliminary transaction data key by using the eye movement-bandwidth association data to obtain measures for adjusting the key encryption parameters;

[0135] Step S343: increasing the number of key verification bits for the preliminary transaction data key based on the hand temperature-signal correlation data, and generating a key verification bit increase measure; adjusting the number of key structure layers for the preliminary transaction data key based on the hand temperature-bandwidth correlation data, and generating a key encryption layer adjustment measure;

[0136] Step S344: Integrate the measures for increasing the overall length of the key, adjusting the key encryption parameters, increasing the number of key verification bits, and adjusting the number of key encryption layers to obtain communication encryption policy data.

[0137] In the embodiment of the present invention, the AES symmetric encryption algorithm (Advanced Encryption Standard) is used to generate an initial key. Specifically, the Python Crypto library is used to generate a 16-byte random key as a preliminary key for transaction data. The key length is increased by the PBKDF2 algorithm in combination with the user's eye movement frequency and signal jitter data, for example, the key length is increased from 128 bits to 256 bits. According to the eye movement-bandwidth association data, encryption parameters such as encryption mode or padding mechanism are dynamically adjusted. Specifically, if low bandwidth usage is detected, the working mode of the AES algorithm is adjusted from CBC (Cipher BlockChaining) mode to GCM (Galois / Counter Mode); according to the hand temperature and signal jitter data, by increasing the entropy value of the key, for example, doubling the entropy value on the basis of the original entropy value; if the hand temperature is abnormal and the bandwidth utilization is low, add a layer of RSA (Rivest-Shamir-Adleman) asymmetric encryption to improve data security; integrate the measures of increasing the overall length of the key, adjusting the key encryption parameters, increasing the number of key verification bits, and adjusting the number of key encryption layers to obtain communication encryption strategy data.

[0138] Preferably, step S4 comprises the following steps:

[0139] Step S41: constructing a communication encryption strategy model according to the communication encryption strategy data to obtain a communication encryption strategy pre-model; and using the communication encryption strategy data to perform model training on the communication encryption strategy pre-model to obtain a communication encryption strategy training model;

[0140] Step S42: performing model cross-validation evaluation on the communication encryption strategy training model to obtain model evaluation data; adjusting model parameters of the communication encryption strategy training model using the model evaluation data to obtain a communication encryption strategy model;

[0141] Step S43: dynamically encrypting the user transaction data based on the communication encryption strategy model to obtain dynamically encrypted transaction data;

[0142] Step S44: Use blockchain technology to upload the dynamically encrypted transaction data to the blockchain node, and distribute the dynamically encrypted transaction data through the blockchain node to generate a transaction data encrypted communication file.

[0143] In the embodiment of the present invention, a machine learning framework (such as TensorFlow or PyTorch) is used to build a communication encryption strategy model, and a neural network is used as the basic architecture of the model; specifically, a neural network containing 3 hidden layers is constructed, each hidden layer contains 64 neurons, and ReLU is used as the activation function; the input layer is the feature vector of the communication encryption strategy data, and the output layer is the encryption strategy result; the model is trained using the cross entropy loss function and the Adam optimizer, and the number of training iterations is set to 1000 times; the training model is evaluated using K-fold cross validation (such as K=5) to determine the generalization ability of the model; specifically, the data is converted into The data set is divided into 5 parts, one part is set aside as the validation set each time, and the rest are used as the training set. After 5 cycles, the average accuracy and loss value are calculated; according to the evaluation results, the number of hidden layers or the number of neurons in the model is adjusted to optimize the model structure; the feature vector of the user transaction data is input into the model, and the model outputs specific encryption strategy parameters, such as key length, encryption algorithm, etc.; according to these parameters, the transaction data is encrypted using the AES-256 encryption algorithm to obtain dynamically encrypted transaction data; the dynamically encrypted transaction data is uploaded to the blockchain node using blockchain technology, and the dynamically encrypted transaction data is distributedly stored through the blockchain node to generate transaction data encrypted communication files.

[0144] Preferably, step S43 includes the following steps:

[0145] Step S431: performing real-time communication transaction status monitoring on user transaction data based on user communication status analysis data to obtain communication transaction status monitoring data;

[0146] Step S432: extracting eye movement signal jitter anomalies from the communication transaction status monitoring data to obtain eye movement signal jitter anomaly monitoring conditions; identifying eye movement bandwidth efficiency anomalies from the communication transaction status monitoring data to generate eye movement bandwidth efficiency anomaly monitoring conditions;

[0147] Step S433: extract hand temperature signal jitter anomaly from the communication transaction status monitoring data to obtain hand temperature signal jitter anomaly monitoring status; identify hand temperature bandwidth efficiency anomaly from the communication transaction status monitoring data to generate hand temperature bandwidth efficiency anomaly monitoring status;

[0148] Step S434: Based on the communication encryption strategy model, a key overall length increase measure is executed for the abnormal monitoring of eye movement signal jitter to generate key length execution data; based on the communication encryption strategy model, a key encryption parameter adjustment measure is executed for the abnormal monitoring of eye movement bandwidth efficiency to obtain encryption parameter adjustment execution data;

[0149] Step S435: Based on the communication encryption strategy model, a key verification bit increase measure is executed for the abnormal monitoring of hand temperature signal jitter, and verification bit execution data is generated; based on the communication encryption strategy model, a key encryption layer number adjustment measure is executed for the abnormal monitoring of hand temperature bandwidth efficiency, and encryption layer number adjustment execution data is obtained;

[0150] Step S436: The key length execution data, encryption parameter adjustment execution data, verification bit number execution data and encryption layer number adjustment execution data are dynamically encrypted and combined to obtain dynamically encrypted transaction data.

[0151] In an embodiment of the present invention, a real-time data stream processing framework (such as Apache Kafka or Apache Flink) is used to monitor user transaction data. For example, through Kafka stream processing, the user's transaction data is sent to the monitoring system in real time to generate communication transaction status monitoring data, including indicators such as transaction delay and success rate; the eye movement signal is smoothed using the sliding average method to identify jitter anomalies, thereby forming an eye movement signal jitter anomaly monitoring situation. At the same time, combined with the bandwidth monitoring data, the abnormal situation of eye movement bandwidth efficiency during the transaction process is identified; the hand temperature data is analyzed in real time to identify the abnormal temperature jitter, and the abnormal hand temperature signal jitter monitoring situation is recorded; at the same time, the relationship between the hand temperature data and the bandwidth usage is analyzed to identify the abnormal situation of the hand temperature bandwidth efficiency; according to the abnormal eye movement signal jitter monitoring situation, the key length is increased (for example, from 256 bits to 512 bits) to generate key length execution data; at the same time, in response to the abnormal eye movement bandwidth efficiency monitoring situation, the encryption parameters (such as encryption algorithm or mode) are adjusted to obtain encryption parameter adjustment execution data; according to the abnormal hand temperature signal jitter monitoring situation, the key verification bit number is increased (for example, from 128 bits to 256 bits) to generate verification bit number execution data; at the same time, in response to the abnormal hand temperature bandwidth efficiency monitoring situation, the number of encryption layers is increased (for example, from 1 layer to 3 layers) to obtain encryption layer number adjustment execution data; the key length execution data, encryption parameter adjustment execution data, verification bit execution data and encryption layer number adjustment execution data are dynamically encrypted and combined to obtain dynamic encrypted transaction data.

[0152] In this specification, a transaction data encryption communication system is provided, which is used to execute the transaction data encryption communication method mentioned above, and the transaction data encryption communication system includes:

[0153] A user biometric feature acquisition module is used to obtain a communication user's biometric feature signal; perform signal encoding conversion on the communication user's biometric feature signal to generate user biometric feature data;

[0154] The communication network performance status evaluation module is used to obtain the communication user network environment information; extract the parameter characteristics of the communication user network environment information to obtain the communication network parameter characteristics; perform communication protocol identification on the communication network parameter characteristics to obtain communication protocol data; perform communication network performance status evaluation on the communication user network environment information according to the communication protocol data to generate network performance status data;

[0155] The communication encryption strategy generation module is used to obtain user transaction data; perform transaction user biometric rheological analysis on user biometric data according to user transaction data to generate biometric rheological data; perform user communication status correlation analysis based on biometric rheological data and network performance status data to obtain user communication status analysis data; generate communication encryption strategy for user transaction data through user communication status analysis data to obtain communication encryption strategy data;

[0156] The dynamic encryption execution module is used to build a communication encryption policy model based on the communication encryption policy data and generate a communication encryption policy model; dynamically encrypt user transaction data based on the communication encryption policy model to obtain dynamically encrypted transaction data; upload the dynamically encrypted transaction data to the blockchain network distributed storage to generate a transaction data encrypted communication file.

[0157] The present invention obtains the biometric signal of the communication user through the user biometric acquisition module and converts it into user biometric data, ensuring the accuracy and security of the user's personal identity authentication during the communication process; this biometric-based authentication mechanism can effectively prevent identity theft and unauthorized access, thereby enhancing the security of the communication system; the communication network performance status evaluation module obtains the network environment information of the communication user, and performs parameter feature extraction and communication protocol identification on it, accurately evaluates the performance status of the communication network, and can monitor and optimize network performance, ensure the stability and efficiency of the communication process, and improve the reliability of data transmission; the communication encryption strategy generation module performs transaction user biometric rheological analysis on user transaction data, combined with network performance Situation data is used to perform correlation analysis of user communication status, clarify the user communication status, and then formulate targeted communication encryption strategy data; the encryption strength is dynamically adjusted according to the user's communication status to improve the security of transaction data; the communication encryption strategy model constructed by the dynamic encryption execution module according to the communication encryption strategy data can dynamically encrypt user transaction data; this dynamic encryption method can adjust the encryption algorithm according to the real-time communication environment and user status to ensure the security of transaction data during transmission; uploading these dynamically encrypted transaction data to the blockchain network for distributed storage can further ensure the immutability and integrity of the data, and the generated transaction data encrypted communication file provides a safe and reliable recording and verification mechanism for the transaction. Therefore, the present invention realizes the correlation analysis of communication transaction status according to the biometric rheological information and network performance situation information of the transaction user through data processing technology, pattern recognition technology and data encryption technology; realizes the dynamic encryption strategy adjustment of transaction data, thereby improving the communication security of transaction data.

[0158] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0159] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A transaction data encryption communication method, characterized in that: The following steps are involved: Step S1: acquiring a biometric signal of a communication user; performing signal encoding conversion on the biometric signal of the communication user to generate user biometric data; Step S2: Acquire communication user network environment information; extract parameter features from the communication user network environment information to obtain communication network parameter features; Perform communication protocol identification on communication network parameter characteristics to obtain communication protocol data; perform communication network performance status evaluation on communication user network environment information based on the communication protocol data to generate network performance status data; Step S3: acquiring user transaction data; performing transaction user biometric rheological analysis on user biometric data according to the user transaction data to generate biometric rheological data; performing user communication status correlation analysis based on biometric rheological data and network performance status data to obtain user communication status analysis data; Generate a communication encryption strategy for user transaction data through user communication status analysis data to obtain communication encryption strategy data; step S3 includes the following steps: Step S31: Acquire user transaction data; extract user transaction behavior features from the user transaction data to obtain user transaction behavior feature data; Step S32: Perform transaction pattern recognition on the user transaction behavior feature data to generate transaction pattern data; perform transaction user biometric rheological analysis on the user biometric data according to the transaction pattern data to generate biometric rheological data; the specific steps of step S32 are: Step S321: Record the transaction amount of the user transaction behavior characteristic data to obtain transaction amount data; monitor the transaction duration of the user transaction behavior characteristic data according to the transaction amount data to obtain the transaction duration; perform transaction pattern matching based on the transaction amount data and the transaction duration to generate transaction pattern data; Step S322: extracting user eye features from the user biometric data to obtain user eye feature information; performing eye tracking on the user eye feature information to generate user eye tracking information; recording the number of blinking features from the user eye tracking information to obtain the number of blinking features of the user; Step S323: Calculate the user's eye movement frequency based on the number of user blinking features according to the transaction duration to generate the user's transaction eye movement frequency; monitor the user's transaction eye movement feature change frequency based on the transaction pattern data to obtain the transaction eye movement feature change frequency; Step S324: performing hand temperature recognition on the user's biometric data to obtain the user's hand temperature data; monitoring the change of the user's transaction hand temperature on the user's hand temperature data based on the transaction mode data to obtain the transaction hand temperature change frequency; Step S325: merging the transaction eye movement feature change frequency and the transaction hand temperature change frequency into the transaction user feature rheological data to generate biometric feature rheological data; Step S33: performing user communication status correlation analysis based on biometric rheological data and network performance status data to obtain user communication status analysis data; Step S34: Generate a communication encryption strategy for the user transaction data through the user communication status analysis data to obtain communication encryption strategy data; Step S4: construct a communication encryption policy model based on the communication encryption policy data to generate a communication encryption policy model; dynamically encrypt the user transaction data based on the communication encryption policy model to obtain dynamically encrypted transaction data; upload the dynamically encrypted transaction data to the blockchain network distributed storage to generate a transaction data encrypted communication file.

2. The transaction data encryption communication method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Acquire the communication user network environment information; perform network signal marking on the communication user network environment information to generate network signal marking data; Step S22: recording the communication network signal parameters of the communication user network environment information according to the network signal tag data to obtain the communication network signal parameters; calculating the network bandwidth of the communication network signal parameters to generate the communication network bandwidth parameters; identifying the regional parameters of the network based on the communication network signal parameters and the communication network bandwidth parameters to obtain the communication network regional parameters; Step S23: integrating the communication network signal parameters, the communication network bandwidth parameters and the communication network area parameters to obtain the communication network parameter characteristics; extracting the network packet header information from the communication network parameter characteristics to generate the network packet header information; reading the protocol identification field from the network packet header information to obtain the protocol identification field; performing communication protocol pattern recognition on the protocol identification field to obtain the communication protocol data; Step S24: Evaluate the communication network performance status of the communication user network environment information according to the communication protocol data to generate network performance status data.

3. The transaction data encryption communication method according to claim 2, characterized in that: Step S24 includes the following steps: Step S241: classifying the communication protocol data into communication protocol types to obtain communication protocol type data; calculating the protocol network throughput of the communication network parameter characteristics according to the communication protocol type data to generate the protocol network throughput; Step S242: recording the network signal strength of the protocol network throughput based on the communication network signal parameter to obtain a network signal strength value; extracting the signal strength peak and valley values ​​of the network signal strength value to obtain a signal strength peak and valley value; Step S243: performing network bandwidth flux measurement on the signal strength peak and valley values ​​according to the communication network bandwidth parameter to generate network bandwidth flux data; performing bandwidth flux change pattern recognition on the network bandwidth flux data to obtain the bandwidth flux change pattern; Step S244: performing network area bandwidth pattern matching on the communication network area parameters through the bandwidth flux change pattern to obtain a network area bandwidth matching pattern; performing network area performance distribution mapping on the network area bandwidth matching pattern based on the signal strength peak and valley values ​​to generate a network area performance distribution map; Step S245: performing regional network signal distribution strength identification on the network regional performance distribution map to obtain regional network signal distribution data; performing network signal stability status evaluation on the network regional performance distribution map based on the regional network signal distribution data to obtain network signal stability status data; Step S246: measuring the regional network bandwidth load on the network regional performance distribution map to generate the regional network bandwidth load; evaluating the network bandwidth utilization status on the network regional performance distribution map based on the regional network bandwidth load to generate network bandwidth utilization status data; Step S247: Use the network signal stability status data and the network bandwidth utilization status data to evaluate the communication network performance status of the communication user network environment information to generate network performance status data.

4. The transaction data encryption communication method according to claim 1, characterized in that: Step S33 includes the following steps: Step S331: performing transaction eye movement abnormal frequency detection on the biometric rheological data to obtain transaction eye movement abnormal frequency data; performing transaction hand temperature abnormal change monitoring on the biometric rheological data to obtain transaction hand temperature abnormal data; Step S332: performing real-time signal fluctuation tracking on the network performance status data to generate signal fluctuation tracking data; performing signal abnormal jitter state identification on the network signal stability status data according to the signal fluctuation tracking data to obtain signal abnormal jitter data; Step S333: performing real-time monitoring of network bandwidth utilization on the network performance status data to generate bandwidth utilization monitoring data; performing low bandwidth utilization detection on the network bandwidth utilization status data through the bandwidth utilization monitoring data to obtain low bandwidth utilization data; Step S334: performing an eye movement signal jitter anomaly correlation on the transaction eye movement abnormal frequency data and the signal abnormal jitter data to obtain eye movement-signal correlation data; performing an eye movement bandwidth efficiency anomaly correlation on the transaction eye movement abnormal frequency data and the bandwidth low utilization rate data to obtain eye movement-bandwidth correlation data; Step S335: Correlate the transaction hand temperature abnormality data and the signal abnormal jitter data with the hand temperature signal jitter abnormality to obtain hand temperature-signal correlation data; correlate the transaction hand temperature abnormality data and the bandwidth low utilization rate data with the hand temperature bandwidth efficiency abnormality to obtain hand temperature-bandwidth correlation data; Step S336: Associating and merging the eye movement-signal correlation data, the eye movement-bandwidth correlation data, the hand temperature-signal correlation data, and the hand temperature-bandwidth correlation data with the user communication status to obtain user communication status analysis data.

5. The transaction data encryption communication method according to claim 1, characterized in that: Step S34 includes the following steps: Step S341: Generate a preliminary transaction data key for the user transaction data according to the transaction mode data to obtain a preliminary transaction data key; Step S342: increasing the overall length of the key for the preliminary transaction data key by using the eye movement-signal association data to obtain measures for increasing the overall length of the key; adjusting the key encryption parameters of the preliminary transaction data key by using the eye movement-bandwidth association data to obtain measures for adjusting the key encryption parameters; Step S343: increasing the number of key verification bits for the preliminary transaction data key based on the hand temperature-signal correlation data, and generating a key verification bit increase measure; adjusting the number of key structure layers for the preliminary transaction data key based on the hand temperature-bandwidth correlation data, and generating a key encryption layer adjustment measure; Step S344: Integrate the measures for increasing the overall length of the key, adjusting the key encryption parameters, increasing the number of key verification bits, and adjusting the number of key encryption layers to obtain communication encryption policy data.

6. The transaction data encryption communication method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: constructing a communication encryption strategy model according to the communication encryption strategy data to obtain a communication encryption strategy pre-model; and using the communication encryption strategy data to perform model training on the communication encryption strategy pre-model to obtain a communication encryption strategy training model; Step S42: performing model cross-validation evaluation on the communication encryption strategy training model to obtain model evaluation data; adjusting model parameters of the communication encryption strategy training model using the model evaluation data to obtain a communication encryption strategy model; Step S43: dynamically encrypting the user transaction data based on the communication encryption strategy model to obtain dynamically encrypted transaction data; Step S44: Use blockchain technology to upload the dynamically encrypted transaction data to the blockchain node, and distribute the dynamically encrypted transaction data through the blockchain node to generate a transaction data encrypted communication file.

7. The transaction data encryption communication method according to claim 6, characterized in that: Step S43 includes the following steps: Step S431: performing real-time communication transaction status monitoring on user transaction data based on user communication status analysis data to obtain communication transaction status monitoring data; Step S432: extracting eye movement signal jitter anomalies from the communication transaction status monitoring data to obtain eye movement signal jitter anomaly monitoring conditions; identifying eye movement bandwidth efficiency anomalies from the communication transaction status monitoring data to generate eye movement bandwidth efficiency anomaly monitoring conditions; Step S433: extract hand temperature signal jitter anomaly from the communication transaction status monitoring data to obtain hand temperature signal jitter anomaly monitoring status; identify hand temperature bandwidth efficiency anomaly from the communication transaction status monitoring data to generate hand temperature bandwidth efficiency anomaly monitoring status; Step S434: Based on the communication encryption strategy model, a key overall length increase measure is executed for the abnormal monitoring of eye movement signal jitter to generate key length execution data; based on the communication encryption strategy model, a key encryption parameter adjustment measure is executed for the abnormal monitoring of eye movement bandwidth efficiency to obtain encryption parameter adjustment execution data; Step S435: Based on the communication encryption strategy model, a key verification bit increase measure is executed for the abnormal monitoring of hand temperature signal jitter, and verification bit execution data is generated; based on the communication encryption strategy model, a key encryption layer number adjustment measure is executed for the abnormal monitoring of hand temperature bandwidth efficiency, and encryption layer number adjustment execution data is obtained; Step S436: The key length execution data, encryption parameter adjustment execution data, verification bit number execution data and encryption layer number adjustment execution data are dynamically encrypted and combined to obtain dynamically encrypted transaction data.

8. A transaction data encryption communication system, characterized in that: Used to execute the transaction data encryption communication method as claimed in claim 1, the transaction data encryption communication system comprises: A user biometric feature acquisition module is used to obtain a communication user's biometric feature signal; perform signal encoding conversion on the communication user's biometric feature signal to generate user biometric feature data; The communication network performance status evaluation module is used to obtain the communication user network environment information; extract the parameter characteristics of the communication user network environment information to obtain the communication network parameter characteristics; perform communication protocol identification on the communication network parameter characteristics to obtain communication protocol data; perform communication network performance status evaluation on the communication user network environment information according to the communication protocol data to generate network performance status data; The communication encryption strategy generation module is used to obtain user transaction data; perform transaction user biometric rheological analysis on user biometric data according to user transaction data to generate biometric rheological data; perform user communication status correlation analysis based on biometric rheological data and network performance status data to obtain user communication status analysis data; generate communication encryption strategy for user transaction data through user communication status analysis data to obtain communication encryption strategy data; The dynamic encryption execution module is used to build a communication encryption policy model based on the communication encryption policy data and generate a communication encryption policy model; dynamically encrypt user transaction data based on the communication encryption policy model to obtain dynamically encrypted transaction data; upload the dynamically encrypted transaction data to the blockchain network distributed storage to generate a transaction data encrypted communication file.

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