Data interaction system based on artificial intelligence algorithm

Abstract

The invention discloses a data interaction system based on an artificial intelligence algorithm, and particularly relates to the technical field of data interaction, which comprises a data acquisition module, a multi-band de-noising module, a multi-band de-noising module, a multi-band de-noising module, a multi-band de-noising module, a multi-band de-noising module and a multi-band de-noising module, and the denoising module is used for dividing input voice data into a plurality of frequency bands and denoising the voice data by applying a self-adaptive denoising algorithm according to noise characteristics of each frequency band. Through cooperative work of a plurality of modules, accurate acquisition, denoising, encrypted transmission, intelligent segmentation and routing, behavior analysis and personalized service and system feedback and optimization of data are realized, the system has the characteristics of high efficiency, safety and personalization, and the accuracy of data interaction and the user experience can be remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of data interaction technology, and more specifically, to a data interaction system based on artificial intelligence algorithm. Background Art

[0002] At present, with the rapid development of computer science, big data, cloud computing and other technologies, artificial intelligence technology has moved from theory to practice and has achieved remarkable results in various fields. The continuous breakthroughs in deep learning, natural language processing, machine learning and other technologies have provided strong support for the intelligence and efficiency of artificial intelligence systems. The rapid development of these technologies has enabled artificial intelligence systems to better understand users' intentions and needs, thereby providing more accurate and personalized services.

[0003] In the field of data interaction, with the popularization of the Internet and the widespread application of smart devices, users' demand for data interaction is growing. Traditional data interaction methods often have problems such as low communication efficiency and long response time, and cannot meet users' needs for efficient and intelligent data interaction. Therefore, a more intelligent and efficient data interaction system is needed to meet user needs.

[0004] Although existing artificial intelligence technologies have achieved certain results in the field of data interaction, there are still some limitations:

[0005] 1. In terms of speech recognition, existing technologies are often affected by factors such as noise and accents, resulting in low recognition accuracy;

[0006] 2. In terms of data transmission, existing technologies often lack adequate security and are vulnerable to hacker attacks and data leaks;

[0007] 3. Existing technologies also have shortcomings in personalized services and are unable to provide accurate services based on users’ actual needs.

[0008] In view of the above situation, the present invention provides a data interaction system based on artificial intelligence algorithm. Summary of the invention

[0009] In order to overcome the above-mentioned defects of the prior art, the present invention provides a data interaction system based on artificial intelligence algorithm to solve the problems raised in the above-mentioned background technology.

[0010] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: based on the artificial intelligence algorithm data interaction system, including a data acquisition module, which collects the user's input data through sensors, microphones and camera devices, and the input data includes voice, images and text;

[0011] The multi-band denoising module is used to divide the input voice data into multiple frequency bands and apply an adaptive denoising algorithm to denoise the voice data according to the noise characteristics of each frequency band. The adaptive denoising algorithm includes:

[0012] The speech data is converted to the frequency domain through fast Fourier transform, and the formula is:

[0013] Among them, X(k) frequency domain representation, that is, the kth frequency component of the voice data in the frequency domain, x(n) time domain voice data, that is, the nth sampling point of the original voice signal, N data length, that is, the total number of sampling points of the voice signal, j imaginary unit, satisfying Complex exponential function, used to map time domain signals to frequency domain;

[0014] A denoising filter with dynamically adjusted parameters is applied to each frequency band, where the parameters are determined according to the frequency band characteristics and the noise intensity. The transfer function of the denoising filter can be expressed as:

[0015] Where H(z) is the transfer function of the filter, z is a variable on the complex plane, representing the delay of the filter, and b j and a i represents the coefficient of the filter, which determines the frequency response of the filter, and M is the order of the filter, which represents the complexity of the filter;

[0016] The denoised data is converted back to the time domain by inverse fast Fourier transform, and the formula is:

[0017] Among them, x(n) represents the nth sampling point reconstructed in the time domain, and X(k) is the value of the kth frequency component in the frequency domain. This formula converts the frequency domain signal back to the time domain to restore the original speech signal;

[0018] The blockchain data encryption and transmission module is used to divide the denoised data into multiple data blocks, encrypt them through the blockchain, and attach a unique identifier generated by the SHA-256 hash algorithm to the data block. The data is transmitted through multiple independently encrypted data channels. The receiving end uses the unique identifier to verify and reassemble the data block.

[0019] Data segmentation and dynamic routing module, which is used to intelligently segment data into multiple data streams according to data type and size, and select the best transmission path based on network conditions and data priority. It also monitors network conditions and data transmission progress in real time and dynamically adjusts transmission paths and priorities.

[0020] The behavior analysis and service module is used to collect and analyze user interaction data, including voice commands and touch operations, and provide personalized services based on the analysis results. The personalized services include but are not limited to intelligent recommendations and voice assistant optimization.

[0021] Preferably, the sensors of the data acquisition module include a temperature sensor, a pressure sensor and a light sensor. The temperature sensor has a measuring range of -20°C-100°C and an accuracy of ±0.5°C. The pressure sensor has a measuring range of 0-1000kPa and an accuracy of ±1kPa. The light sensor has a sensitivity of 0.1lux.

[0022] The microphone pickup range is 0.5-10 meters, the sensitivity is -38dBV / Pa, the camera device resolution is 1080p-4K, and the frame rate is 30-60fps.

[0023] Preferably, in the multi-band denoising module, the method for adjusting the parameters of the denoising filter includes:

[0024] Calculate the signal-to-noise ratio of each frequency band using the formula: Among them, P signal is the signal power, P noise is the noise power;

[0025] Determine the type and parameters of the denoising filter based on the SNR, including the order and cutoff frequency of the filter;

[0026] Dynamically adjust the parameters of the denoising filter to adapt to changes in different frequency bands and different noise intensities.

[0027] Preferably, in the blockchain data encryption and transmission module, the encryption method of the data block includes: encrypting the data block using the ECC asymmetric encryption algorithm, generating a public key and a private key pair, broadcasting the public key and the data block through the blockchain network, storing the private key at the sending end or in a secure key management service, and the receiving end using the public key to verify the integrity of the data block and using the private key to decrypt the data block.

[0028] Preferably, in the data segmentation and dynamic routing module, the data stream transmission path selection method includes: evaluating the network conditions of multiple data channels, including bandwidth, delay and packet loss rate, selecting the optimal data channel combination as the transmission path according to data priority and network conditions, and continuously monitoring the network conditions during the transmission process, and dynamically adjusting the transmission path as needed.

[0029] Preferably, in the behavior analysis and service module, the user behavior analysis method includes: collecting user interaction data, including voice commands, touch operations and usage time, using machine learning algorithms to classify and cluster user interaction data, identifying user behavior patterns and preferences, and providing personalized services based on user behavior patterns and preferences, including intelligent recommendations and voice assistant optimization.

[0030] Preferably, it also includes a feedback and optimization module for receiving user feedback on system performance and service quality, and optimizing and adjusting the system algorithms and parameters according to the feedback results.

[0031] Preferably, a method of a data interaction system is also included, comprising the following steps:

[0032] S1, data collection, the data collection module collects the user's voice, image and text input data through sensors, microphones and camera devices;

[0033] S2, multi-band denoising, the multi-band denoising module divides the collected voice data into multiple frequency bands, converts them to the frequency domain using fast Fourier transform, applies an adaptive denoising algorithm based on the noise characteristics of each frequency band, processes them through a denoising filter with dynamically adjusted parameters, and then converts them back to the time domain via inverse fast Fourier transform;

[0034] S3, data encryption and transmission. The blockchain data encryption and transmission module divides the denoised data into multiple data blocks, encrypts them using an asymmetric encryption algorithm and generates a public key and a private key pair. The public key is transmitted together with the data block, and the private key is properly stored. The data block is attached with a unique identifier through the blockchain and transmitted through multiple independently encrypted data channels. The receiving end uses the unique identifier to verify and reassemble the data block;

[0035] S4, data segmentation and routing, the data segmentation and dynamic routing module intelligently segments data into multiple data streams according to the type and size of the data, evaluates the network conditions of bandwidth, delay and packet loss rate of multiple data channels, selects the best transmission path based on data priority, and monitors the network status and data transmission progress in real time during the transmission process, and dynamically adjusts the transmission path and priority;

[0036] S5. Behavior Analysis and Services: The behavior analysis and service module collects and analyzes the user's voice commands and touch operation interaction data, classifies and clusters them using machine learning algorithms to identify the user's behavior patterns and preferences, and provides personalized services based on this, such as intelligent recommendations and voice assistant optimization;

[0037] S6. Feedback and optimization. The feedback and optimization module receives user feedback on system performance and service quality, and optimizes and adjusts the system's algorithms and parameters based on the feedback results to continuously improve the system's overall performance and user experience.

[0038] Technical effects and advantages of the present invention:

[0039] By integrating the multi-band denoising module and the blockchain data encryption and transmission module, efficient processing and secure transmission of user input data are achieved. The multi-band denoising module uses an adaptive denoising algorithm to accurately identify and remove noise in voice data, thereby improving the accuracy and clarity of voice recognition. At the same time, the blockchain data encryption and transmission module ensures the security and integrity of data during transmission by segmenting data blocks, encrypting processing, and attaching unique identifiers, effectively preventing data leakage and tampering. In addition, the data segmentation and dynamic routing module intelligently selects the optimal transmission path based on network conditions and data priority, further improving the efficiency and reliability of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a system diagram of the present invention.

[0041] Figure 2 It is a flow chart of the present invention.

[0042] Figure 3 This is a multi-band denoising process flow chart of the present invention.

[0043] Figure 4 Schematic diagram of data segmentation and dynamic routing of the present invention.

[0044] The accompanying drawings are marked as follows: 1. Data acquisition module; 2. Multi-band denoising module; 3. Blockchain data encryption and transmission module; 4. Data segmentation and dynamic routing module; 5. Behavior analysis and service module; 6. Feedback and optimization module. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The present invention proposes a data interaction system based on artificial intelligence algorithm. The implementation scenario can be in an intelligent medical assistant system, where medical staff frequently interact with the system, including voice entry of medical records, image upload of examination results, and text input of diagnosis suggestions. The system needs to accurately process data to assist medical decision-making and optimize services, such as ensuring clear voice, secure and efficient data transmission, and providing personalized medical assistance during remote diagnosis. This is the implementation scenario of the present invention.

[0047] The system integrates data acquisition module 1, multi-band denoising module 2, blockchain data encryption and transmission module 3, data segmentation and dynamic routing module 4, behavior analysis and service module 5, and feedback and optimization module 6 to improve the accuracy and security of data interaction, provide personalized services, and continuously optimize system performance. The specific modules are as follows:

[0048] Data collection module 1 collects user input data through sensors, microphones and camera devices. The input data includes voice, images and text. The sensors include temperature sensors, pressure sensors and light sensors. The temperature sensor has a measurement range of -20°C to 100°C and an accuracy of ±0.5°C. The pressure sensor has a range of 0-1000kPa and an accuracy of ±1kPa. The light sensor has a sensitivity of 0.1lux. The microphone has a sound pickup range of 0.5-10 meters and a sensitivity of -38dBV / Pa. The camera device has a resolution of 1080p to 4K and a frame rate of 30-60fps.

[0049] The application environment can be described as: in hospital wards, -20℃-100℃, ±0.5℃ accuracy temperature sensors monitor the equipment ambient temperature to prevent overheating and overload, 0-1000kPa, ±1kPa accuracy pressure sensors monitor the pressure of infusion bags to ensure stable infusion, 0.1lux sensitivity light sensors intelligently adjust the light according to the light to protect the health of patients' eyes, 0.5-10 meters pickup, -38dBV / Pa sensitivity microphones accurately collect medical voice commands and patient symptom descriptions, 1080p-4K resolution, 30-60fps frame rate high-definition cameras capture wounds and medical records, real-time capture of medical operation gestures, and multiple devices collaborate to collect multi-dimensional data, injecting intelligent power into the medical process.

[0050] The multi-band denoising module 2 is used to divide the input voice data into multiple frequency bands, and apply an adaptive denoising algorithm to denoise the voice data according to the noise characteristics of each frequency band. The adaptive denoising algorithm includes: converting the voice data into the frequency domain through fast Fourier transform, and its formula is: Among them, X(k) frequency domain representation, that is, the kth frequency component of the voice data in the frequency domain, x(n) time domain voice data, that is, the nth sampling point of the original voice signal, N data length, that is, the total number of sampling points of the voice signal, j imaginary unit, satisfying Complex exponential function, used to map time domain signals to frequency domain;

[0051] A denoising filter with dynamically adjusted parameters is applied to each frequency band, where the parameters are determined according to the frequency band characteristics and the noise intensity. The transfer function of the denoising filter can be expressed as:

[0052] Where H(z) is the transfer function of the filter, z is a variable on the complex plane, representing the delay of the filter, and b j and a i Represents the coefficient of the filter, which determines the frequency response of the filter. M is the order of the filter, indicating the complexity of the filter;

[0053] The denoised data is converted back to the time domain by inverse fast Fourier transform, and the formula is:

[0054] Among them, x(n) represents the nth sampling point reconstructed in the time domain, and X(k) is the value of the kth frequency component in the frequency domain. This formula converts the frequency domain signal back to the time domain to restore the original speech signal;

[0055] In the multi-band denoising module 2, the parameter adjustment method of the denoising filter includes:

[0056] Calculate the signal-to-noise ratio of each frequency band using the formula: Among them, P signal is the signal power, P noise is the noise power;

[0057] Determine the type and parameters of the denoising filter based on the SNR, including the order and cutoff frequency of the filter;

[0058] Dynamically adjust the parameters of the denoising filter to adapt to changes in different frequency bands and different noise intensities;

[0059] The application environment can be described as follows: in a noisy emergency room, voice commands are mixed with equipment alarms and personnel noise. The system analyzes the noise according to the frequency band characteristics, such as low-frequency humming of equipment 50-200Hz and high-frequency noise of human voice 1kHz-5kHz. The voice spectrum is analyzed by fast Fourier transform, and a denoising filter is set for each frequency band. In case of strong noise, the filter parameters are accurately set according to the signal-to-noise ratio, such as the SNR of a certain frequency band is 5dB. The high-order band-stop filter with an order of 8 is selected and the cutoff frequency is fine-tuned according to the noise frequency band. Dynamic optimization is performed every 5 milliseconds, and clear voice is restored through inverse transformation. After denoising, the voice recognition is improved by 40%, which clears the way for remote consultation and command transmission.

[0060] The blockchain data encryption and transmission module 3 is used to divide the denoised data into multiple data blocks, encrypt them through the blockchain, and attach a unique identifier generated by the SHA-256 hash algorithm to the data block, and transmit them through multiple independently encrypted data channels. The receiving end uses the unique identifier to verify and reorganize the data block. In the blockchain data encryption and transmission module 3, the encryption method of the data block includes: using the ECC asymmetric encryption algorithm to encrypt the data block, generating a public key and a private key pair, broadcasting the public key and the data block through the blockchain network, and storing the private key at the sending end or in a secure key management service. The receiving end uses the public key to verify the integrity of the data block and uses the private key to decrypt the data block;

[0061] The application environment can be described as follows: medical data is related to privacy. The system divides the medical records and image data blocks after denoising, encrypts them into public key and private key pairs with ECC, broadcasts the public key with the data block, and stores the private key in the hospital encryption server or professional KMS. A SHA-256 hash unique identifier is attached to the data block, and the data is transmitted to the cloud storage or telemedicine end through a multi-UDP optimized encrypted channel. The receiving end verifies the identifier to prevent tampering and decrypts it with the private key to ensure that the entire medical data flow is encrypted, traceable, and leak-proof. The data tampering recognition rate exceeds 99.9%, strictly guarding the lifeline of medical privacy.

[0062] The data segmentation and dynamic routing module 4 is used to intelligently segment the data into multiple data streams according to the type and size of the data, and select the best transmission path based on the network status and data priority, while monitoring the network status and data transmission progress in real time, and dynamically adjusting the transmission path and priority. In the data segmentation and dynamic routing module 4, the transmission path selection method of the data stream includes: evaluating the network status of multiple data channels, including bandwidth, delay and packet loss rate, selecting the best data channel combination as the transmission path according to the data priority and network status, and continuously monitoring the network status during the transmission process, and dynamically adjusting the transmission path as needed;

[0063] The application environment can be described as follows: Medical data is diverse, and is intelligently segmented by type: medical record text, image, real-time physiological monitoring flow, size: text KB, image resolution and related traffic. For example, in live surgery, high-definition images are layered according to frame rate, and network bandwidth, latency and packet loss rate are evaluated in real time. Surgical control instructions give priority to low-latency channels, and images choose high-bandwidth stable paths. When the network fluctuates, such as a sudden drop in bandwidth of 30%, the intelligent routing switches the path within 10 milliseconds to ensure zero latency for medical instructions, stable and accurate data transmission, 35% increase in bandwidth utilization, and 1% control of packet loss rate.

[0064] The behavior analysis and service module 5 is used to collect and analyze the user's interaction data, including voice commands and touch operations, and provide personalized services based on the analysis results. The personalized services include but are not limited to intelligent recommendations and voice assistant optimization. In the behavior analysis and service module 5, the user behavior analysis method includes: collecting the user's interaction data, including voice commands, touch operations and usage time, using machine learning algorithms to classify and cluster the user interaction data, identifying the user's behavior patterns and preferences, and providing personalized services based on the user's behavior patterns and preferences, including intelligent recommendations and voice assistant optimization;

[0065] The application environment can be described as: collecting medical operation data, such as voice command semantics: voice recognition accuracy of 98%, device touch trajectory: coordinate accuracy of 0.05mm, usage time period, and using deep neural network clustering analysis. For example, analyzing a doctor's habit of morning ward rounds and preference for imaging diagnosis, the system intelligently recommends similar case references and imaging analysis tools, optimizes the voice assistant interaction logic, actively pushes ward rounds tasks in the morning, and turns up the volume according to habit, thereby improving medical efficiency and experience. The accuracy rate of personalized recommendations is 88%.

[0066] The feedback and optimization module 6 is used to receive user feedback on system performance and service quality, and optimize and adjust the system algorithms and parameters according to the feedback results.

[0067] The application environment can be described as follows: the system has multiple feedback ports, and medical staff can evaluate performance on the interface, make suggestions to the community, and report abnormalities in the log. For example, if a remote consultation encounters a freeze, NLP sentiment analysis and clustering can be used to locate network adaptation problems, and the algorithm can be optimized based on this, such as adjusting routing strategy weights, parameters, and iteration functions. Closed-loop optimization is carried out based on performance indicators and satisfaction surveys, with an annual function iteration of 25%, driving the system to adapt to medical innovation needs.

[0068] The workflow of the present invention is as follows:

[0069] S1, data collection, the data collection module 1 collects the user's voice, image and text input data through sensors, microphones and camera devices;

[0070] S2, multi-band denoising, the multi-band denoising module 2 divides the collected voice data into multiple frequency bands, converts them into the frequency domain using fast Fourier transform, applies an adaptive denoising algorithm according to the noise characteristics of each frequency band, processes them through a denoising filter with dynamically adjusted parameters, and then converts them back to the time domain via inverse fast Fourier transform;

[0071] S3, data encryption and transmission, the blockchain data encryption and transmission module 3 divides the denoised data into multiple data blocks, encrypts them using an asymmetric encryption algorithm and generates a public key and a private key pair, transmits the public key together with the data block, and properly stores the private key. The data block is attached with a unique identifier through the blockchain and transmitted through multiple independently encrypted data channels. The receiving end uses the unique identifier to verify and reassemble the data block;

[0072] S4, data segmentation and routing, the data segmentation and dynamic routing module 4 intelligently segments the data into multiple data streams according to the type and size of the data, evaluates the network conditions of the bandwidth, delay and packet loss rate of multiple data channels, selects the best transmission path in combination with the data priority, and monitors the network status and data transmission progress in real time during the transmission process, and dynamically adjusts the transmission path and priority;

[0073] S5, behavior analysis and service, the behavior analysis and service module 5 collects and analyzes the user's voice command and touch operation interaction data, classifies and clusters them using machine learning algorithms to identify the user's behavior patterns and preferences, and provides personalized services based on this, such as intelligent recommendations and voice assistant optimization;

[0074] S6, feedback and optimization, the feedback and optimization module 6 receives user feedback on system performance and service quality, and optimizes and adjusts the system algorithms and parameters according to the feedback results to continuously improve the overall performance of the system and user experience.

[0075] In summary, this embodiment provides a data interaction system based on artificial intelligence algorithm. The system realizes accurate data collection, denoising, encrypted transmission, intelligent segmentation and routing, behavior analysis and personalized services, and system feedback and optimization through the collaborative work of multiple modules. The system is efficient, secure, and personalized, and can significantly improve the accuracy of data interaction and user experience.

[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. Based on artificial intelligence algorithm data interaction system, it is characterized by: It includes a data collection module (1) for collecting user input data through sensors, microphones and camera devices, the input data including voice, images and text; The multi-band denoising module (2) is used to divide the input voice data into multiple frequency bands and apply an adaptive denoising algorithm to denoise the voice data according to the noise characteristics of each frequency band. The adaptive denoising algorithm includes: The speech data is converted to the frequency domain through fast Fourier transform, and the formula is: Among them, X(k) frequency domain representation, that is, the kth frequency component of the voice data in the frequency domain, x(n) time domain voice data, that is, the nth sampling point of the original voice signal, N data length, that is, the total number of sampling points of the voice signal, j imaginary unit, satisfying Complex exponential function, used to map time domain signals to frequency domain; A denoising filter with dynamically adjusted parameters is applied to each frequency band, where the parameters are determined according to the frequency band characteristics and the noise intensity. The transfer function of the denoising filter can be expressed as: Where H(z) is the transfer function of the filter, z is a variable on the complex plane, representing the delay of the filter, and b j and a i represents the coefficient of the filter, which determines the frequency response of the filter, and M is the order of the filter, which represents the complexity of the filter; The denoised data is converted back to the time domain by inverse fast Fourier transform, and the formula is: Among them, x(n) represents the nth sampling point reconstructed in the time domain, and X(k) is the value of the kth frequency component in the frequency domain. This formula converts the frequency domain signal back to the time domain to restore the original speech signal; The blockchain data encryption and transmission module (3) is used to divide the denoised data into multiple data blocks, encrypt them through the blockchain, attach a unique identifier generated by the SHA-256 hash algorithm to the data block, transmit them through multiple independently encrypted data channels, and the receiving end uses the unique identifier to verify and reassemble the data block; A data segmentation and dynamic routing module (4), which is used to intelligently segment data into multiple data streams according to the type and size of the data, and select the best transmission path based on the network status and data priority, while monitoring the network status and data transmission progress in real time, and dynamically adjusting the transmission path and priority; The behavior analysis and service module (5) is used to collect and analyze user interaction data, including voice commands and touch operations, and provide personalized services based on the analysis results. The personalized services include but are not limited to intelligent recommendations and voice assistant optimization.

2. The data interaction system based on artificial intelligence algorithm according to claim 1 is characterized by: The sensors of the data acquisition module (1) include a temperature sensor, a pressure sensor and a light sensor. The temperature sensor has a measuring range of -20°C to 100°C and an accuracy of ±0.5°C. The pressure sensor has a measuring range of 0-1000 kPa and an accuracy of ±1 kPa. The light sensor has a sensitivity of 0.1 lux. The microphone pickup range is 0.5-10 meters, the sensitivity is -38dBV / Pa, the camera device resolution is 1080p-4K, and the frame rate is 30-60fps.

3. The data interaction system based on artificial intelligence algorithm according to claim 1 is characterized in that: In the multi-band denoising module (2), the method for adjusting the parameters of the denoising filter comprises: Calculate the signal-to-noise ratio of each frequency band using the formula: Among them, P signal is the signal power, P noise is the noise power; Determine the type and parameters of the denoising filter based on the SNR, including the order and cutoff frequency of the filter; Dynamically adjust the parameters of the denoising filter to adapt to changes in different frequency bands and different noise intensities.

4. The data interaction system based on artificial intelligence algorithm according to claim 1 is characterized in that: In the blockchain data encryption and transmission module (3), the encryption method of the data block includes: using the ECC asymmetric encryption algorithm to encrypt the data block, generating a public key and a private key pair, broadcasting the public key and the data block through the blockchain network, storing the private key at the sending end or in a secure key management service, and the receiving end using the public key to verify the integrity of the data block and using the private key to decrypt the data block.

5. The data interaction system based on artificial intelligence algorithm according to claim 1 is characterized in that: In the data segmentation and dynamic routing module (4), the data stream transmission path selection method includes: evaluating the network conditions of multiple data channels, including bandwidth, delay and packet loss rate, selecting the optimal data channel combination as the transmission path according to data priority and network conditions, and continuously monitoring the network conditions during the transmission process, and dynamically adjusting the transmission path as needed.

6. The data interaction system based on artificial intelligence algorithm according to claim 1 is characterized in that: In the behavior analysis and service module (5), the user behavior analysis method includes: collecting user interaction data, including voice commands, touch operations and usage time, using a machine learning algorithm to classify and cluster the user interaction data, identifying user behavior patterns and preferences, and providing personalized services based on user behavior patterns and preferences, including intelligent recommendations and voice assistant optimization.

7. The data interaction system based on artificial intelligence algorithm according to claim 1 is characterized in that: It also includes a feedback and optimization module (6) for receiving user feedback on system performance and service quality, and optimizing and adjusting the system algorithms and parameters according to the feedback results.

8. The data interaction system based on artificial intelligence algorithm according to claim 1 is characterized by: Also included is a method of a data interaction system, comprising the following steps: S1, data collection, the data collection module (1) collects the user's voice, image and text input data through sensors, microphones and camera devices; S2, multi-band denoising, the multi-band denoising module (2) divides the collected speech data into multiple frequency bands, converts them into the frequency domain using fast Fourier transform, applies an adaptive denoising algorithm according to the noise characteristics of each frequency band, processes them through a denoising filter with dynamically adjustable parameters, and then converts them back to the time domain through inverse fast Fourier transform; S3, data encryption and transmission, the blockchain data encryption and transmission module (3) divides the denoised data into multiple data blocks, encrypts them using an asymmetric encryption algorithm and generates a public key and a private key pair, transmits the public key together with the data block, and properly stores the private key. The data block is attached with a unique identifier through the blockchain and transmitted through multiple independently encrypted data channels. The receiving end uses the unique identifier to verify and reassemble the data block; S4, data segmentation and routing, the data segmentation and dynamic routing module (4) intelligently segments the data into multiple data streams according to the type and size of the data, evaluates the network conditions of the bandwidth, delay and packet loss rate of multiple data channels, selects the best transmission path based on the data priority, and monitors the network conditions and data transmission progress in real time during the transmission process, and dynamically adjusts the transmission path and priority; S5. Behavior analysis and service. The behavior analysis and service module (5) collects and analyzes the user's voice command and touch operation interaction data, classifies and clusters them using machine learning algorithms to identify the user's behavior patterns and preferences, and provides personalized services based on this, such as intelligent recommendations and voice assistant optimization; S6. Feedback and optimization. The feedback and optimization module (6) receives user feedback on system performance and service quality, and optimizes and adjusts the system's algorithms and parameters based on the feedback results to continuously improve the system's overall performance and user experience.

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