Live broadcast interactive real-time transaction system and method

Abstract

The invention provides a live broadcast interactive real-time transaction method and a live broadcast interactive real-time transaction system, and aims to improve the interaction efficiency and risk control capability of financial product live broadcast transactions, and comprises the following steps: encoding real-time market data into SEI metadata, embedding the SEI metadata into a video stream, and realizing ultra-low delay synchronous transmission based on SRT and QUIC protocols. Voice recognition, video OCR and text recognition modules are integrated, and real-time risk recognition and early warning of live broadcast content are achieved. Meanwhile, a real-time feature calculation pipeline based on Flink is constructed, personalized investment suggestions are output through a dynamic risk model, and a transaction closed loop is formed. The method supports multi-mode authentication, including living body detection, voiceprint comparison and equipment fingerprint verification, so as to guarantee transaction security and compliance.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular, to a live interactive real-time transaction system and method, and more particularly, to a live interactive real-time transaction system and method. Background Art

[0002] Currently, online live marketing of financial products faces multiple technical bottlenecks, which seriously affect user experience and business efficiency. The main problems include:

[0003] The time and space misalignment between real-time interaction and transaction execution: The existing system uses a separate transmission architecture (RTMP+HTTP), which results in a timing deviation of 300-800 milliseconds between the live broadcast screen and the transaction signal. This delay causes users to frequently jump between the live broadcast window and the transaction page, with an average of 3.2 jumps, resulting in 28% of potential transactions being lost;

[0004] Risk measurement and market volatility response hysteresis: Currently, a Spark-based batch processing framework is used, and the update cycle of risk parameters exceeds 15 minutes. Under extreme market conditions, this mechanism can cause the risk exposure calculation error to be as high as ±19%. For example, during the market volatility in February 2024, 23% of wealth management products experienced instantaneous deviations between real-time net value and risk rating.

[0005] Discrete detection blind spots in compliance control: The existing compliance monitoring system uses a 5-minute interval to conduct content sampling and detection, and the accuracy of speech-to-text conversion is less than 92%, resulting in 18% of sensitive words being missed. As a result, 42% of live broadcast periods contain unidentified illegal content, and the average handling delay reaches 8 minutes and 12 seconds.

[0006] The above technical bottlenecks need to be resolved urgently to improve the interactive efficiency, risk management capabilities and compliance monitoring level of online live broadcasts of financial products.

[0007] Patent document CN113626711B (application number: 202110936851.7) discloses a method and device for recommending live videos of mobile banking, which relates to the field of financial technology; the method comprises: taking the intersection of mobile banking search records and mobile banking transaction records to obtain a first intersection, and taking the intersection of mobile banking search records and mobile banking common transaction records to obtain a second intersection; determining the priority value of each banking business according to the first intersection, the second intersection, mobile banking search records, mobile banking transaction records and mobile banking common transaction records; determining the recommendation value of each live video to be recommended according to the priority value of the banking business associated with each live video to be recommended, and the distance between the bank branch associated with the live video to be recommended and the user; determining the live video recommended to the user according to the recommendation value of each live video to be recommended. Summary of the invention

[0008] In view of the deficiencies in the prior art, the object of the present invention is to provide a live interactive real-time trading method and system.

[0009] A live interactive real-time trading method provided by the present invention includes:

[0010] Step S1: Encode real-time market data into auxiliary enhancement information SEI metadata and embed it into the live video stream;

[0011] Step S2: Perform real-time scanning on the video stream and execute risk identification through speech recognition, video recognition, and text detection;

[0012] Step S3: The intelligent live broadcast engine synchronously transmits the video stream that meets the preset conditions and the market data to the client through the transmission SRT protocol and the UDP Internet connection QUIC protocol;

[0013] Step S4: The client performs risk assessment based on the received market data, generates investment recommendations according to the assessment results, and completes the trading loop accordingly.

[0014] Preferably, the risk identification in step S2 includes:

[0015] Step S2.1: Perform a full-scale scan of the live video stream through the regulatory sandbox system;

[0016] Step S2.2: Based on the connection time series classification loss function CTC-loss, construct a speech recognition model and a video image OCR detection model, analyze the speech and image content respectively, and trigger a risk warning when content that does not meet the preset standards is detected;

[0017] Step S2.3: Construct a text detection model and perform compliance judgment on the recognized text. If there is abnormal text, generate an alarm message.

[0018] Preferably, the risk assessment in step S4 includes:

[0019] Step S4.1: Collect data including exchange market data, macroeconomic indicators, and user trading behavior data;

[0020] Step S4.2: Input the collected data into a real-time feature calculation pipeline built based on Apache Flink to generate feature data including price volatility, trading volume mutation, buy-sell pressure ratio, and correlation matrix;

[0021] Step S4.3: Input the feature data into a dynamic risk adaptation model for risk rating output, and the risk rating is used to generate investment recommendations;

[0022] Among them, the risk adaptation model is adjusted in real time based on including market impact factors, user risk preference values, and product volatility parameters, and is comprehensively evaluated through preset weight factors.

[0023] Preferably, the video stream, market data, and trading instructions are calibrated with unified timestamps based on the Precision Time Protocol (PTP) to achieve microsecond-level synchronous processing.

[0024] Preferably, the method further includes: at least one type of identity verification needs to be completed before trading execution, including: live detection verification, voiceprint comparison verification, and device fingerprint verification;

[0025] The live detection verification includes: detecting the user's facial micro-expressions and physiological characteristics through an integrated visible light camera, infrared sensor, and 3D structured light module;

[0026] The voiceprint comparison verification includes: extracting a 256-dimensional voiceprint vector through the ECAPA-TDNN model and comparing the real-time reading content;

[0027] The device fingerprint verification includes: collecting software and hardware characteristics and encrypting them using the SM4 algorithm, and generating a unique irreversible device fingerprint through SHA3-512.

[0028] A live interactive real-time trading system provided by the present invention includes:

[0029] Module M1: Encoding real-time market data into supplementary enhanced information (SEI) metadata and embedding it into the live video stream;

[0030] Module M2: Scanning the video stream in real time and performing risk identification through speech recognition, video recognition, and text detection;

[0031] Module M3: The intelligent live broadcast engine synchronously transmits the video stream that meets the preset conditions and the market data to the client through the transmission SRT protocol and the UDP Internet connection QUIC protocol;

[0032] Module M4: The client performs risk assessment based on the received market data, generates investment recommendations according to the assessment results, and completes the trading loop accordingly.

[0033] Preferably, the risk identification in the module M2 includes:

[0034] Module M2.1: Performing a full-scale scan of the live video stream through the regulatory sandbox system;

[0035] Module M2.2: Constructing a speech recognition model and a video image OCR detection model based on the connectionist temporal classification loss function (CTC-loss), analyzing the speech and image content respectively, and triggering a risk warning when content that does not meet the preset standards is detected;

[0036] Module M2.3: Build a text detection model, and perform compliance judgment on the recognized text. If there is abnormal text, generate an alarm message.

[0037] Preferably, the risk assessment in the module M4 includes:

[0038] Module M4.1: Collect exchange market data, macroeconomic indicators, and user trading behavior data;

[0039] Module M4.2: Input the collected data into a real-time feature calculation pipeline built based on Apache Flink to generate feature data including price volatility, trading volume mutation, buy-sell pressure ratio, and correlation matrix;

[0040] Module M4.3: Input the feature data into a dynamic risk adaptation model for risk rating output, and the risk rating is used to generate investment recommendations;

[0041] Among them, the risk adaptation model is adjusted in real time based on market impact factors, user risk preference values, and product volatility parameters, and comprehensively evaluated through preset weight factors.

[0042] Preferably, the video stream, market data, and trading instructions are calibrated with unified timestamps based on the Precision Time Protocol (PTP) to achieve microsecond-level synchronous processing.

[0043] Preferably, the system further includes: at least one type of identity verification needs to be completed before trading execution, including: live detection verification, voiceprint comparison verification, and device fingerprint verification;

[0044] The live detection verification includes: detecting the user's facial micro-expressions and physiological characteristics through an integrated visible light camera, infrared sensor, and 3D structured light module;

[0045] The voiceprint comparison verification includes: extracting 256-dimensional voiceprint vectors through the ECAPA-TDNN model and comparing the real-time reading content;

[0046] The device fingerprint verification includes: collecting software and hardware characteristics and encrypting them using the SM4 algorithm, and generating a unique irreversible device fingerprint through SHA3-512.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. The present invention overcomes the problems of trading fragmentation, risk lag, and compliance blind spots in traditional live trading systems, realizes the deep integration of financial product live broadcasts and real-time trading, and provides users with a safe, compliant, and efficient investment experience;

[0049] 2. The live broadcast engine of the present invention transmits the host's explanation video stream through the SRT protocol and embeds the SEI metadata into the video stream, and the two are transmitted synchronously with ultra-low latency.

[0050] 3. The present invention dynamically adjusts the risk rating based on the obtained real-time market data, thereby improving the risk matching accuracy and completing the assistance for trading decisions.

[0051] 4. In the present invention, live broadcast, market conditions, and trading instructions are synchronized at the microsecond level through a unified timestamp, avoiding the problem of trading fragmentation in traditional systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Other features, objects, and advantages of the present invention will become more apparent by reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0053] Figure 1 It is a schematic diagram of a live interactive real-time trading system.

[0054] Figure 2 It is a schematic diagram of the dual-loop feedback optimization of the dynamic risk control engine.

[0055] Figure 3 It is a schematic diagram of multi-modal authentication. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those of ordinary skill in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0057] Embodiment 1

[0058] According to a live interactive real-time trading method provided by the present invention, as Figure 1 shown, it includes:

[0059] Step S1: Encode the real-time market data into auxiliary enhancement information SEI metadata and embed it into the live video stream;

[0060] Step S2: Perform real-time scanning on the video stream and execute risk identification through speech recognition, video recognition, and text detection;

[0061] Step S3: The intelligent live broadcast engine synchronously transmits the video stream that meets the preset conditions and the market data to the client through the transmission SRT protocol and the UDP Internet connection QUIC protocol;

[0062] Step S4: The client performs risk assessment based on the received market data, generates investment recommendations according to the assessment results, and completes the trading loop accordingly.

[0063] In this embodiment, the live broadcast engine transmits the host's explanatory video stream through the SRT protocol and embeds the SEI metadata into the video stream, achieving ultra-low latency synchronous transmission of <500ms for both.

[0064] Dynamically adjust the risk rating based on the acquired real-time market data, thereby improving the risk matching accuracy and completing the trading decision-making assistance.

[0065] In this embodiment, live broadcast, market conditions, and trading instructions adopt the PTP precision clock protocol to achieve microsecond-level synchronization through a unified timestamp, avoiding the trading fragmentation problem in traditional systems; at the same time, realizing the real-time and accuracy of risk assessment.

[0066] Specifically, step S2 includes: performing real-time scanning on the video stream and using a voice recognition engine optimized by CTC-loss to identify risks in the video stream;

[0067] Step S2.1: Perform a full scan of the live video stream through the regulatory sandbox system; in this embodiment, perform 100% real-time scanning on the live content through the regulatory sandbox system;

[0068] Step S2.2: Build a voice recognition model and a video image OCR detection model based on the connectionist temporal classification loss function CTC-loss, and use the built voice recognition model and video image OCR detection model to detect the voice and image content respectively. When voices and / or videos that do not meet the preset requirements are detected, an alarm is triggered;

[0069] Step S2.3: Build a text detection model and use the built text detection model to detect the recognized text. When text that does not meet the preset requirements is detected, an alarm is triggered.

[0070] In this embodiment, alarms are triggered in a timely manner for voices, videos, and text content that do not meet the preset requirements to ensure compliance.

[0071] Specifically, step S4 includes: calculating feature data based on market data through a real-time feature calculation pipeline built by Flink, and obtaining the risk rating corresponding to the feature data through a dynamic risk adaptation model; providing personalized investment recommendations based on the risk rating corresponding to the feature data.

[0072] Specifically, the calculation of feature data based on market data through a real-time feature calculation pipeline built by Flink, and obtaining the risk rating corresponding to the feature data through a dynamic risk adaptation model includes:

[0073] Obtain exchange market data, macroeconomic indicators, and user trading behavior data;

[0074] Preprocess the obtained exchange market data, macroeconomic indicators, and user trading behavior data to obtain preprocessed exchange market data, macroeconomic indicators, and user trading behavior data;

[0075] Calculate feature data including price volatility, trading volume mutation, buy-sell pressure ratio, and correlation matrix based on the preprocessed exchange market data, macroeconomic indicators, and user trading behavior data through a feature calculation pipeline;

[0076] Obtain the risk rating corresponding to the feature data for the feature data through a dynamic risk adaptation model;

[0077] The method further includes: Live video streams, market data, and trading instructions use the PTP Precision Clock Protocol to achieve microsecond-level synchronization through a unified timestamp.

[0078] The method further includes: During the transaction process, perform security verification and execute the transaction after passing the verification;

[0079] The security verification includes at least one of liveness detection verification, voiceprint comparison verification, and device fingerprint verification;

[0080] The liveness detection verification includes: Integrate a visible light camera, an infrared sensor, and a 3D structured light module to achieve liveness detection verification by detecting facial micro-expressions, subcutaneous blood flow characteristics, and three-dimensional depth information; During the liveness detection verification process, based on multiple randomly generated action verification instructions, analyze the authenticity by a forgery detection model constructed based on a deep convolutional network, including the coherence and biomechanical characteristics of the user's actions;

[0081] The voiceprint comparison verification includes: Use the ECAPA-TDNN network architecture to construct a deep speaker recognition model, and use the constructed deep speaker recognition model to extract a speaker embedding vector containing 256-dimensional voiceprint features;

[0082] During the voiceprint comparison verification process, obtain voice information based on multiple randomly generated alphanumeric combination reading texts in real time, integrate multi-channel beamforming technology to achieve environmental noise suppression, and then perform voiceprint information comparison;

[0083] The device fingerprint verification includes: Obtain the user's fingerprint information, and use the SM4 algorithm to encrypt 23 features to generate an irreversible device fingerprint through SHA3-512.

[0084] In this example, the problems of transaction fragmentation, risk lag, and compliance blind spots in traditional live trading systems are overcome, achieving a deep integration of financial product live broadcasts and real-time transactions, and providing users with a safe, compliant, and efficient investment experience.

[0085] The present invention also provides a live interactive real-time trading system, which can be implemented by executing the process steps of the live interactive real-time trading method. That is, those skilled in the art can understand the live interactive real-time trading method as a preferred embodiment of the live interactive real-time trading system.

[0086] Embodiment 2

[0087] Embodiment 2 is a preferred example of Embodiment 1

[0088] A live interactive real-time trading system provided according to the present invention, as Figure 1 shown, includes:

[0089] The intelligent live broadcast engine transmits the host's explanation video stream through the SRT protocol;

[0090] The real-time market condition module embeds market data into the video stream in the form of SEI metadata;

[0091] The combination of the two achieves ultra-low latency synchronous transmission of <500ms;

[0092] The real-time market condition module real-time pushes market fluctuation data to the dynamic risk engine;

[0093] The dynamic risk engine dynamically adjusts the risk rating according to market data;

[0094] The risk matching accuracy is improved through a dynamic risk adaptation algorithm; wherein, the dynamic risk control engine implements a double-loop feedback optimization and a continuous learning mechanism, as Figure 2 shown.

[0095] Global monitoring of the regulatory sandbox system:

[0096] Perform 100% real-time scanning of the live content;

[0097] The detection results are real-time fed back to the intelligent live broadcast engine and the intelligent investment advisor engine;

[0098] The illegal content is automatically disposed of to ensure compliance.

[0099] Interactive support of the intelligent investment advisor engine:

[0100] Respond to user queries based on the financial knowledge graph;

[0101] Provide personalized investment advice according to the risk assessment results;

[0102] Collaborate with the real-time trading module to complete trading decision-making assistance;

[0103] As Figure 3 shown, during the trading process, multi-modal authentication is performed, including: live detection + voiceprint comparison + device fingerprint;

[0104] Among them, the live detection includes:

[0105] Multi-spectral imaging system: Integrate visible light cameras, infrared sensors, and 3D structured light modules. By detecting facial micro-expressions, subcutaneous blood flow characteristics, and three-dimensional depth information, it can effectively resist attack methods such as photos / videos / 3D masks;

[0106] Dynamic instruction response: Randomly generate verification instructions containing 2-4 actions (such as "Please blink and then shake your head to the right"), and judge authenticity by analyzing the coherence and biomechanical characteristics of the user's actions;

[0107] Anti-fraud detection engine: A forgery detection model built based on a deep convolutional network, which can identify more than 3,000 known attack patterns such as printing attacks and screen replay attacks.

[0108] The said voiceprint comparison includes:

[0109] Deep speaker recognition model: Adopt the ECAPA-TDNN network architecture to extract speaker embedding vectors containing 256-dimensional voiceprint features;

[0110] Dynamic text challenge mechanism: The system generates a reading text containing a 6-8-digit random alphanumeric combination in real time (such as "Trust Product A3B9") to ensure that the voice samples for each verification are unpredictable;

[0111] Environmental noise suppression: Integrate multi-channel beamforming technology, and still maintain a recognition accuracy of 98.7% in an environment where the signal-to-noise ratio ≥ -5dB;

[0112] The said device fingerprint verification includes:

[0113] Feature collection: Hardware features: CPU instruction set fingerprint, GPU rendering mode, screen parameters; Software features: font list, browser plugin hash, system time zone configuration; Network features: TCP window size, IPv6 address configuration mode; Behavioral features: touch screen pressure curve, gyroscope jitter frequency;

[0114] Fingerprint generation: Use the national secret SM4 algorithm to encrypt 23 items of features; Generate an irreversible device fingerprint through SHA3-512; Fingerprint update mechanism: Automatically refresh 50% of the feature items every 24 hours.

[0115] The real-time trading module receives the authentication result and only allows verified users to execute transactions; It supports the continuous authentication mechanism during the trading process.

[0116] In this embodiment, a time-space synchronization mechanism is implemented:

[0117] Live broadcasts, market quotes, and trading instructions achieve microsecond-level synchronization through a unified timestamp, avoiding the problem of trading fragmentation in traditional systems;

[0118] Implement risk closed-loop control:

[0119] Market data → risk assessment → investment advice → trading execution forms a closed loop, achieving the real-time and accuracy of risk assessment;

[0120] Implement multi-layer security protection: The authentication module and the trading module cooperate to build a multi-layer security barrier; the regulatory sandbox provides full-process compliance guarantee;

[0121] Data fusion and decision-making:

[0122] The data of each module is fused and analyzed in the risk engine, and the intelligent investment advisor provides decision support based on the comprehensive data. Those skilled in the art know that in addition to implementing the system, device, and its various modules provided by the present invention in the form of pure computer-readable program code, the method steps can be logically programmed to make the system, device, and its various modules provided by the present invention be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers, etc. to achieve the same program. Therefore, the system, device, and its various modules provided by the present invention can be considered as a kind of hardware component, and the modules included therein for implementing various programs can also be regarded as the structure within the hardware component; the modules for implementing various functions can also be regarded as both software programs for implementing the method and the structure within the hardware component.

[0123] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the above specific implementation manners, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Without conflict, the embodiments of the present application and the features in the embodiments can be combined arbitrarily with each other.

Claims

1. A live interactive real-time trading method, characterized in that, Including: Step S1: Encode real-time market data into auxiliary enhanced information SEI metadata and embed it into the live video stream; Step S2: Perform real-time scanning on the video stream and execute risk identification through speech recognition, video recognition, and text detection; Step S3: The intelligent live broadcast engine synchronously transmits the video stream that meets the preset conditions and the market data to the client through the transmission SRT protocol and the UDP Internet connection QUIC protocol; Step S4: The client performs risk assessment based on the received market data, generates investment suggestions according to the assessment results, and completes the trading loop accordingly.

2. The method according to claim 1, wherein The risk identification in step S2 includes: Step S2.1: Perform full-scale scanning on the live video stream through the regulatory sandbox system; Step S2.2: Construct a speech recognition model and a video image OCR detection model based on the connection timing classification loss function CTC-loss, analyze the speech and image content respectively, and trigger a risk warning when content that does not meet the preset standards is detected; Step S2.3: Construct a text detection model and perform compliance judgment on the identified text. If there is abnormal text, an alarm message is generated.

3. The method according to claim 1, wherein The risk assessment in step S4 includes: Step S4.1: Collect data including exchange market data, macroeconomic indicators, and user trading behavior data; Step S4.2: Input the collected data into a real-time feature calculation pipeline built based on Apache Flink to generate feature data including price volatility, trading volume mutation, buy-sell pressure ratio, and correlation matrix; Step S4.3: Input the feature data into a dynamic risk adaptation model for risk rating output, and the risk rating is used to generate investment suggestions; Among them, the risk adaptation model is adjusted in real time based on including market impact factors, user risk preference values, and product volatility parameters, and is comprehensively evaluated through preset weight factors.

4. The method according to claim 1, wherein The video stream, market data, and trading instructions are uniformly timestamped based on the Precision Time Protocol PTP to achieve microsecond-level synchronous processing.

5. The method according to claim 1, wherein The method further includes: At least one type of identity verification needs to be completed before trading execution, including: live detection verification, voiceprint comparison verification, and device fingerprint verification; The live detection verification includes: Detecting the user's facial micro-expressions and physiological characteristics through an integrated visible light camera, infrared sensor, and 3D structured light module; The voiceprint comparison verification includes: Extracting 256-dimensional voiceprint vectors through the ECAPA-TDNN model and comparing the real-time reading content; The device fingerprint verification includes: Collecting software and hardware characteristics and encrypting them using the SM4 algorithm, and generating a unique irreversible device fingerprint through SHA3-512.

6. A live interactive real-time trading system, characterized in that, Including: Module M1: Encode real-time market data into auxiliary enhanced information SEI metadata and embed it into the live video stream; Module M2: Perform real-time scanning on the video stream and execute risk identification through speech recognition, video recognition, and text detection; Module M3: The intelligent live broadcast engine synchronously transmits the video stream that meets the preset conditions and the market data to the client through the transmission SRT protocol and the UDP Internet connection QUIC protocol; Module M4: The client performs risk assessment based on the received market data, generates investment recommendations according to the assessment results, and completes the trading loop accordingly.

7. The system according to claim 6, wherein The risk identification in the module M2 includes: Module M2.1: Perform a full scan of the live video stream through the regulatory sandbox system; Module M2.2: Construct a speech recognition model and a video image OCR detection model based on the connection timing classification loss function CTC-loss, analyze the speech and image content respectively, and trigger a risk warning when content that does not meet the preset standards is detected; Module M2.3: Construct a text detection model and perform compliance judgment on the recognized text. If there is abnormal text, generate an alarm message.

8. The system according to claim 6, wherein The risk assessment in the module M4 includes: Module M4.1: Collect exchange market data, macroeconomic indicators, and user trading behavior data; Module M4.2: Input the collected data into a real-time feature calculation pipeline built based on Apache Flink to generate feature data including price volatility, trading volume mutation, buy-sell pressure ratio, and correlation matrix; Module M4.3: Input the feature data into a dynamic risk adaptation model for risk rating output, and the risk rating is used to generate investment recommendations; Among them, the risk adaptation model is adjusted in real time based on the market impact factor, user risk preference value, and product volatility parameter, and is comprehensively evaluated through a preset weight factor.

9. The system according to claim 6, wherein The video stream, market data, and trading instructions are calibrated with unified timestamps based on the Precision Time Protocol PTP to achieve microsecond-level synchronous processing.

10. The system according to claim 6, characterized in that, The system also includes: At least one type of identity verification needs to be completed before trading execution, including: live detection verification, voiceprint comparison verification, and device fingerprint verification; The live detection verification includes: Detect the user's facial micro-expressions and physiological characteristics through an integrated visible light camera, infrared sensor, and 3D structured light module; The voiceprint comparison verification includes: Extract 256-dimensional voiceprint vectors through the ECAPA-TDNN model and compare the real-time reading content; The device fingerprint verification includes: Collect software and hardware characteristics and encrypt them using the SM4 algorithm, and generate a unique irreversible device fingerprint through SHA3-512.

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