High-definition video transmission optimization and encryption method based on wifi7 chip module

Abstract

The present invention discloses a high-definition video transmission optimization and encryption method based on a Wi-Fi 7 chip module, relating to the technical field of data transmission resource allocation, including: real-time collection of load data, interference data, and signal strength data for each frequency band; dynamic selection of the optimal frequency band combination for parallel transmission; adjustment of the modulation method and allocation of network resources based on signal quality; prediction of load changes through real-time monitoring of network load; dynamic adjustment of the preamble perforation pattern based on interference conditions; generation of dynamic keys through a hardware security module and distribution to terminal devices through a secure channel, establishing a key lifecycle management system; and continuous authentication and authorization of devices and users using a zero-trust architecture. By adjusting the resource allocation strategy in advance, video transmission problems caused by sudden load changes are effectively avoided. Furthermore, a key lifecycle management system is established to manage key generation, distribution, use, and destruction, thereby improving the security of key management.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission resource allocation, and in particular to a high-definition video transmission optimization and encryption method based on a WiFi7 chip module. Background Art

[0002] With the accelerating digital age, demand for high-definition video transmission in many fields is exploding. In home entertainment scenarios, 8K ultra-high-definition video and VR / AR content are becoming increasingly popular. Users have extremely high expectations for video smoothness and image clarity, requiring a stable and high-speed network to ensure the ultimate audio-visual experience. In the corporate office sector, remote video conferencing and online collaborative work have become the norm. Real-time transmission of high-definition video is crucial for efficient communication and decision-making. In the security monitoring industry, high-definition video surveillance systems require real-time transmission of large amounts of high-definition video data to promptly identify safety hazards and take appropriate measures.

[0003] However, existing network technologies face numerous challenges in transmitting high-definition video. Traditional Wi-Fi technology struggles to provide stable and high-speed transmission when faced with multiple connected devices and complex interference environments, leading to issues such as video freezes and image quality degradation. Furthermore, high-definition video data contains a large amount of sensitive information, such as family privacy, corporate secrets, and medical data, making data security an increasingly prominent concern. Summary of the Invention

[0004] In order to solve the above technical problems, a high-definition video transmission optimization and encryption method based on the wifi7 chip module is provided. This technical solution solves the problems raised in the above background technology.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] High-definition video transmission optimization and encryption method based on wifi7 chip module, including:

[0007] Deploy network monitoring sensors to collect real-time load data, interference data, and signal strength data for each frequency band;

[0008] Based on real-time network load, interference conditions, and signal strength, the optimal frequency band combination is dynamically selected for parallel transmission, and the bandwidth allocation of each frequency band is dynamically adjusted according to the bandwidth utilization of the frequency band;

[0009] Dynamically bind at least one channel based on the network environment, adjust the modulation method based on signal quality, and allocate network resources;

[0010] Establish a load forecasting model to predict load changes by monitoring network load in real time and adjust resource allocation strategies in advance;

[0011] Dynamically adjust the preamble puncturing mode based on the interference situation to shield the interfered frequency band and retain the available frequency band;

[0012] Generate dynamic keys through hardware security modules and distribute them to terminal devices through secure channels. Establish a key lifecycle management system to manage key generation, distribution, use, and destruction.

[0013] Adopt zero-trust architecture to continuously authenticate and authorize devices and users, combine identity authentication methods, and optimize end-to-end encryption performance through hardware acceleration.

[0014] Preferably, the dynamic key is generated by the hardware security module and distributed to the terminal device through a secure channel, and a key lifecycle management system is established to manage the generation, distribution, use and destruction of the key, specifically including:

[0015] Deploy a hardware security module to prevent unauthorized access, initialize and configure the hardware security module, and set the administrator password and security policy;

[0016] Based on security standards and business requirements, select the key length to be generated and define the conditions and frequency for key generation;

[0017] The hardware security module uses an internal true random number generator to generate key material and a key derivation function to derive the final key from the key material;

[0018] Select the Secure Sockets Layer (SSL) cryptographic communication framework (CFC) encryption channel as the secure channel for key distribution and configure digital certificates for both ends of the channel.

[0019] Configure security parameters for the encryption channel, including encryption algorithm, key length, and session timeout;

[0020] Encapsulate the key in a key container and add metadata to the key container, wherein the metadata includes the key ID, generation time, and validity period;

[0021] Select a symmetric encryption algorithm to encrypt the key container and use a hardware security module to manage the encryption key;

[0022] The terminal device initiates a key distribution request through the client of the key management system, and the key management system verifies the distribution request;

[0023] The key management system transmits the encrypted key container to the terminal device through a secure channel, monitors the key transmission process in real time, and records the transmission log;

[0024] After receiving the key container, the terminal device performs integrity verification and decrypts the key container using the pre-configured decryption key to obtain the key;

[0025] When encryption and decryption operations are required, the terminal device obtains the key from the storage environment and uses the corresponding encryption algorithm to perform the operation;

[0026] When the key reaches the preset validity period, the destruction process is automatically triggered, and the authorized administrator initiates a key destruction request, verifies the destruction request, and locates the key that needs to be destroyed.

[0027] Preferably, the use of a zero-trust architecture to continuously authenticate and authorize devices and users, combined with identity authentication methods, and optimizing end-to-end encryption performance through hardware acceleration specifically includes:

[0028] Based on the principle of least privilege, ensure that each user and device has only the minimum permissions required to complete their tasks. Based on the principle of continuous verification, perform continuous identity authentication and authorization checks on each access request.

[0029] Based on business logic and security requirements, the network is divided into three micro-segments: application layer, data layer, and management layer. Each micro-segment maintains security isolation and prohibits lateral movement within the network.

[0030] The first factor of user authentication is recorded as username and password, and the password is stored using an encrypted hash algorithm;

[0031] The second factor of user authentication is recorded as dynamic token and biometric identification. Dynamic token is generated using hardware token, and the user enters the token value for verification. Biometric identification technologies such as fingerprint and facial recognition are also supported.

[0032] The third factor of user authentication is recorded as a user behavior model, which records the user's normal behavior pattern;

[0033] Collect user information context, device information context, and environmental information context. The user information context includes user identity, role, and permission information; the device information context includes device type, status, and location information; and the environmental information context includes access time, location, and network status information.

[0034] The policy decision point makes authorization decisions based on the collected context information, performs risk assessment on access requests, and dynamically adjusts authorization decisions;

[0035] The policy execution point executes the access control policy based on the authorization decision of the policy decision point, allowing or denying the access request;

[0036] By utilizing the parallel computing capability of the hardware accelerator, at least one encryption and decryption task can be processed simultaneously to improve the task processing speed.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] Dynamically adjust the bandwidth allocation of each frequency band according to the bandwidth utilization rate of the frequency band, improve the utilization efficiency of spectrum resources, and ensure the smooth transmission of high-definition video. Adjust the modulation method according to the signal quality and allocate network resources, so as to make full use of network resources and improve the quality and efficiency of video transmission. By monitoring the network load in real time and predicting load changes, adjust the resource allocation strategy in advance, and effectively avoid video transmission problems caused by sudden load changes. By establishing a key lifecycle management system to manage the generation, distribution, use and destruction of keys, the security of key management is improved and key leakage is prevented. A zero-trust architecture is used to continuously authenticate and authorize devices and users. Combined with identity authentication methods, end-to-end encryption performance is optimized through hardware acceleration, which enhances network security and ensures that only legitimate devices and users can access video data. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of the high-definition video transmission optimization and encryption method based on the wifi7 chip module of the present invention;

[0040] Figure 2 This is a flow chart of the method for dynamically selecting the optimal frequency band combination for parallel transmission according to the present invention;

[0041] Figure 3 This is a flow chart of a method for adjusting a modulation mode according to signal quality according to the present invention;

[0042] Figure 4 This is a flow chart of a method for predicting load changes by real-time monitoring of network load according to the present invention;

[0043] Figure 5 This is a flow chart of a method for dynamically adjusting a preamble puncture mode based on interference conditions according to the present invention;

[0044] Figure 6 A flow chart of a method for establishing a key lifecycle management system according to the present invention;

[0045] Figure 7 This is a flow chart of the method for continuously authenticating and authorizing devices and users using a zero-trust architecture according to the present invention. DETAILED DESCRIPTION

[0046] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0047] Reference Figure 1 As shown in the figure, the high-definition video transmission optimization and encryption method based on the WiFi7 chip module includes:

[0048] Deploy network monitoring sensors to collect real-time load data, interference data, and signal strength data for each frequency band;

[0049] Based on real-time network load, interference conditions, and signal strength, the optimal frequency band combination is dynamically selected for parallel transmission, and the bandwidth allocation of each frequency band is dynamically adjusted according to the bandwidth utilization of the frequency band;

[0050] Dynamically bind at least one channel based on the network environment, adjust the modulation method based on signal quality, and allocate network resources;

[0051] Establish a load forecasting model to predict load changes by monitoring network load in real time and adjust resource allocation strategies in advance;

[0052] Dynamically adjust the preamble puncturing mode based on the interference situation to shield the interfered frequency band and retain the available frequency band;

[0053] Generate dynamic keys through hardware security modules and distribute them to terminal devices through secure channels. Establish a key lifecycle management system to manage key generation, distribution, use, and destruction.

[0054] Adopt zero-trust architecture to continuously authenticate and authorize devices and users, combine identity authentication methods, and optimize end-to-end encryption performance through hardware acceleration.

[0055] Reference Figure 2 As shown in the figure, based on the real-time network load, interference situation and signal strength, the optimal frequency band combination is dynamically selected for parallel transmission. The bandwidth allocation of each frequency band is dynamically adjusted according to the bandwidth utilization of the frequency band. Specifically, it includes:

[0056] Analyze the time series data of bandwidth utilization of each frequency band, identify and mark the peak and trough periods;

[0057] Draw a curve showing how interference intensity changes with frequency band, and analyze the temporal pattern of interference occurrence;

[0058] Statistically calculate the distribution of signal strength and signal-to-noise ratio in each frequency band to evaluate the stability of signal quality;

[0059] Based on the device location and the collected signal strength data, a signal attenuation model is established to predict the signal coverage range;

[0060] A frequency band scoring model was established, with load indicators determined as bandwidth utilization, number of device connections, and traffic congestion level; interference indicators determined as interference intensity, interference frequency band ratio, and interference duration; and signal quality indicators determined as signal strength, signal-to-noise ratio, and signal strength fluctuation range;

[0061] Based on historical data, the correlation between each scoring indicator and transmission performance is analyzed, and the corresponding weights of each scoring indicator are preset;

[0062] List at least one frequency band combination and exclude combinations containing interfering frequency bands. Score and rank each frequency band combination according to a frequency band scoring model.

[0063] Evaluate the synergistic effects caused by interference between frequency bands and the performance improvement during parallel transmission, and select the combination with the best overall performance.

[0064] Steps for establishing a signal attenuation model: Collect device location information such as GPS coordinates or relative position and signal strength data for the corresponding frequency band. Based on the collected data, establish a signal attenuation model such as a free space path loss model or a logarithmic distance path loss model to predict the signal coverage range. Dynamically adjust the bandwidth allocation strategy for each frequency band based on the frequency band score and bandwidth utilization, allocating more bandwidth to frequency bands with high scores and low utilization, and reducing the bandwidth of frequency bands with low scores and high utilization.

[0065] Reference Figure 3 As shown, dynamically binding at least one channel according to the network environment, adjusting the modulation mode according to the signal quality, and allocating network resources specifically include:

[0066] Measure the received signal strength indicator of each channel to evaluate signal coverage and quality;

[0067] Based on changes in the network environment, terminal devices are dynamically bound to the optimal channel or backup channel through software configuration;

[0068] Count the bit error rate and packet loss rate during transmission, and set threshold ranges for the bit error rate and packet loss rate indicators based on historical data;

[0069] Classifying signal quality according to the set threshold range, wherein the signal quality includes excellent, good, fair, and poor;

[0070] Based on the signal quality level, select the modulation method that matches it;

[0071] When the signal quality is excellent, select 64-order quadrature amplitude modulation to increase the transmission rate;

[0072] When the signal quality is good, select 16-order quadrature amplitude modulation to improve noise resistance;

[0073] When the signal quality is average, select quadrature phase shift keying to reduce the difficulty of data transmission;

[0074] When the signal quality is poor, binary phase shift keying is selected to improve transmission reliability;

[0075] Collect bandwidth and latency requirements of each user or device, and count the total load on the current network, the load of each channel, and the number of device connections;

[0076] Based on the importance and bandwidth requirements of users or devices, set at least one priority level to give priority to meeting the resource needs of high-priority users or devices;

[0077] Dynamically adjust resource allocation strategies based on changes in network load and user demand.

[0078] When the signal quality is excellent, select 64-order orthogonal amplitude modulation, which can transmit 6 bits of information in one symbol. Compared with low-order modulation methods, it can significantly improve the transmission rate; when the signal quality is good, select 16-order orthogonal amplitude modulation, which can transmit 4 bits of information in one symbol. Compared with 64-order orthogonal amplitude modulation, it has better noise resistance and can maintain high transmission efficiency when the signal quality slightly decreases; when the signal quality is average, select orthogonal phase shift keying, which can transmit 2 bits of information in one symbol. Its modulation and demodulation are relatively simple, which can reduce the difficulty of data transmission and adapt to environments with average signal quality; when the signal quality is poor, select binary phase shift keying, which can transmit 1 bit of information in one symbol. Although the transmission rate is low, it has the strongest anti-interference ability and can ensure reliable data transmission under very poor signal quality.

[0079] Reference Figure 4 As shown in the figure, a load prediction model is established to predict load changes by real-time monitoring of network load and to adjust resource allocation strategies in advance. Specifically, the following are included:

[0080] Collect historical network load data and record timestamps for each piece of data;

[0081] Remove outliers, missing values, and duplicate data from the load data, and normalize the remaining data to obtain the load data set;

[0082] Constructing an autoregressive differential moving average model for load forecasting, wherein the autoregressive differential moving average model includes an autoregressive model part, a differential process part, and a moving average model part;

[0083] The autoregressive model part eliminates the influence of observations of at least one period in the past on the current value by processing the autoregressive part of the time series;

[0084] The difference process makes the non-stationary time series stationary through second-order difference processing, eliminating the trend and seasonal factors in the time series;

[0085] The moving average model part eliminates the impact of past forecast errors on current values ​​by processing the moving average part of the time series;

[0086] Divide the load data set into training set, validation set and test set, use the training set to train the model, adjust the model parameters to optimize the prediction performance, and use the validation set to verify the model and evaluate the generalization ability of the model;

[0087] Use the trained load forecasting model to predict real-time data and obtain the load change trend for at least a period of time in the future;

[0088] Based on the network load prediction results, set the threshold range for resource allocation;

[0089] Develop at least one resource allocation strategy under load conditions, increase bandwidth, adjust the number of device connections, and optimize traffic routing.

[0090] The autoregressive model part determines the autoregressive order p, that is, the impact of the observation values ​​of the past p periods on the current value, by analyzing the autocorrelation of historical load data; the difference process part uses first-order or second-order difference processing to make the non-stationary time series reach a stable state, eliminate the trend and seasonal factors in the time series, and determine the difference order d; the moving average model part determines the moving average order q, that is, the impact of the forecast error of the past q periods on the current value, by analyzing the autocorrelation of the forecast error.

[0091] Reference Figure 5 As shown, the preamble puncturing mode is dynamically adjusted based on the interference situation to shield the interfered frequency band and retain the available frequency band. Specifically, the following are performed:

[0092] Real-time collection of interference intensity, interference frequency range and interference duration data for each frequency band;

[0093] Identify interference source types through signal feature analysis, including co-channel interference, adjacent-channel interference, and external device interference;

[0094] Evaluate the interference level of each frequency band based on interference intensity, duration, and impact range;

[0095] Setting the puncture position corresponding to the interfered frequency band in the preamble to shield all signals on the interfered frequency band, and identifying the available frequency band based on the interference analysis results;

[0096] The puncturing pattern is adjusted to retain the preamble information and communication resources on the available frequency band and allocate them to at least one user or device according to actual needs.

[0097] Based on the interference assessment results, the affected frequency band is determined, and the puncture position corresponding to the interfered frequency band is set in the preamble. The preamble is part of the Wi-Fi signal and is used by the receiver for signal synchronization and channel estimation. If the 5.2GHz-5.3GHz frequency band is determined to be interfered with, the signal position corresponding to this frequency range in the preamble is punctured, that is, all signals in this frequency band are blocked. In specific implementation, the preamble generation algorithm can be modified. When generating the preamble, the signal portion corresponding to the interfered frequency band is set to zero or a specific marking method is used, so that the receiver can identify and ignore these signals.

[0098] Reference Figure 6 As shown, dynamic keys are generated through the hardware security module and distributed to terminal devices through a secure channel. A key lifecycle management system is established to manage the generation, distribution, use, and destruction of keys, including:

[0099] Deploy a hardware security module to prevent unauthorized access, initialize and configure the hardware security module, and set the administrator password and security policy;

[0100] Based on security standards and business requirements, select the key length to be generated and define the conditions and frequency for key generation;

[0101] The hardware security module uses an internal true random number generator to generate key material and a key derivation function to derive the final key from the key material;

[0102] Select the Secure Sockets Layer (SSL) cryptographic communication framework (CFC) encryption channel as the secure channel for key distribution and configure digital certificates for both ends of the channel.

[0103] Configure security parameters for the encryption channel, including encryption algorithm, key length, and session timeout;

[0104] Encapsulate the key in a key container and add metadata to the key container, wherein the metadata includes the key ID, generation time, and validity period;

[0105] Select a symmetric encryption algorithm to encrypt the key container and use a hardware security module to manage the encryption key;

[0106] The terminal device initiates a key distribution request through the client of the key management system, and the key management system verifies the distribution request;

[0107] The key management system transmits the encrypted key container to the terminal device through a secure channel, monitors the key transmission process in real time, and records the transmission log;

[0108] After receiving the key container, the terminal device performs integrity verification and decrypts the key container using the pre-configured decryption key to obtain the key;

[0109] When encryption and decryption operations are required, the terminal device obtains the key from the storage environment and uses the corresponding encryption algorithm to perform the operation;

[0110] When the key reaches the preset validity period, the destruction process is automatically triggered, and the authorized administrator initiates a key destruction request, verifies the destruction request, and locates the key that needs to be destroyed.

[0111] The parameters of the key derivation function are: DK is the final derived key, PRF is the pseudo-random function, Password is the original password used to generate the key, Salt is the salt value, a random number or random bit sequence used to increase the security of the password, c is the number of iterations, which indicates the number of times the password hash is repeatedly calculated, dkLen is the expected key length in bits, the key derivation function connects the password and the salt value as the initial input of the pseudo-random function, and calculates the derived key through multiple rounds of iterations. Each round of iteration will take the result of the previous round as input and pass it to the pseudo-random function together with the password, salt value and current iteration number. For each round of iteration i, the XOR chain of c traversals is calculated, and the results of each round of iteration are spliced ​​together to form the final derived key DK.

[0112] Reference Figure 7 As shown, a zero-trust architecture is used to continuously authenticate and authorize devices and users. Combined with identity authentication methods, hardware acceleration is used to optimize end-to-end encryption performance. Specifically, the following features are included:

[0113] Based on the principle of least privilege, ensure that each user and device has only the minimum permissions required to complete their tasks. Based on the principle of continuous verification, perform continuous identity authentication and authorization checks on each access request.

[0114] Based on business logic and security requirements, the network is divided into three micro-segments: application layer, data layer, and management layer. Each micro-segment maintains security isolation and prohibits lateral movement within the network.

[0115] The first factor of user authentication is recorded as username and password, and the password is stored using an encrypted hash algorithm;

[0116] The second factor of user authentication is recorded as dynamic token and biometric identification. Dynamic token is generated using hardware token, and the user enters the token value for verification. Biometric identification technologies such as fingerprint and facial recognition are also supported.

[0117] The third factor of user authentication is recorded as a user behavior model, which records the user's normal behavior pattern;

[0118] Collect user information context, device information context, and environmental information context. The user information context includes user identity, role, and permission information; the device information context includes device type, status, and location information; and the environmental information context includes access time, location, and network status information.

[0119] The policy decision point makes authorization decisions based on the collected context information, performs risk assessment on access requests, and dynamically adjusts authorization decisions;

[0120] The policy execution point executes the access control policy based on the authorization decision of the policy decision point, allowing or denying the access request;

[0121] By utilizing the parallel computing capability of the hardware accelerator, at least one encryption and decryption task can be processed simultaneously to improve the task processing speed.

[0122] The principle of least privilege evaluates the permissions of all users and devices, clarifies the tasks they need to complete and the corresponding minimum permission sets, uses access control lists or role-based access control systems to ensure that users and devices only have the minimum permissions required to complete tasks, and regularly reviews permission allocation to ensure that permissions are consistent with actual needs; the principle of continuous verification embeds a continuous verification mechanism in all access requests, through continuous identity authentication and authorization checks, uses real-time monitoring tools to detect abnormal access behavior, such as access during non-working hours, unusual operation sequences, etc., and triggers a re-verification process.

[0123] Furthermore, the present solution also proposes a computer-readable storage medium on which a computer-readable program is stored. When the computer-readable program is called, the above-mentioned high-definition video transmission optimization and encryption method based on the wifi7 chip module is executed.

[0124] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state disk (SSD).

[0125] To sum up, the advantages of the present invention are: dynamically adjusting the bandwidth allocation of each frequency band according to the bandwidth utilization rate of the frequency band, improving the utilization efficiency of spectrum resources, ensuring the smooth transmission of high-definition video, adjusting the modulation method according to the signal quality, allocating network resources, making full use of network resources, improving the quality and efficiency of video transmission, predicting load changes by real-time monitoring of network load, adjusting resource allocation strategies in advance, and effectively avoiding video transmission problems caused by sudden load changes; by establishing a key lifecycle management system, managing the generation, distribution, use and destruction of keys, improving the security of key management, preventing key leakage, adopting a zero-trust architecture to continuously authenticate and authorize devices and users, combining identity authentication methods, optimizing end-to-end encryption performance through hardware acceleration, enhancing network security, and ensuring that only legitimate devices and users can access video data.

[0126] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. The high-definition video transmission optimization and encryption method based on the wifi7 chip module is characterized in that: include: Deploy network monitoring sensors to collect real-time load data, interference data, and signal strength data for each frequency band; Based on real-time network load, interference conditions, and signal strength, the optimal frequency band combination is dynamically selected for parallel transmission, and the bandwidth allocation of each frequency band is dynamically adjusted according to the bandwidth utilization of the frequency band; Dynamically bind at least one channel based on the network environment, adjust the modulation mode based on signal quality, and allocate network resources; Establish a load forecasting model to predict load changes by monitoring network load in real time and adjust resource allocation strategies in advance; Dynamically adjust the preamble puncturing mode based on the interference situation to shield the interfered frequency band and retain the available frequency band; Generate dynamic keys through hardware security modules and distribute them to terminal devices through secure channels. Establish a key lifecycle management system to manage key generation, distribution, use, and destruction. Adopt zero-trust architecture to continuously authenticate and authorize devices and users, combine identity authentication methods, and optimize end-to-end encryption performance through hardware acceleration.

2. The high-definition video transmission optimization and encryption method based on the wifi7 chip module according to claim 1 is characterized in that: The method of dynamically selecting the optimal frequency band combination for parallel transmission based on real-time network load, interference conditions, and signal strength, and dynamically adjusting the bandwidth allocation of each frequency band according to the bandwidth utilization of the frequency band specifically includes: Analyze the time series data of bandwidth utilization of each frequency band, identify and mark the peak and trough periods; Draw a curve showing how interference intensity changes with frequency band, and analyze the temporal pattern of interference occurrence; Statistically calculate the distribution of signal strength and signal-to-noise ratio in each frequency band to evaluate the stability of signal quality; Based on the device location and the collected signal strength data, a signal attenuation model is established to predict the signal coverage range; A frequency band scoring model was established, with load indicators determined as bandwidth utilization, number of device connections, and traffic congestion level; interference indicators determined as interference intensity, interference frequency band ratio, and interference duration; and signal quality indicators determined as signal strength, signal-to-noise ratio, and signal strength fluctuation range; Based on historical data, the correlation between each scoring indicator and transmission performance is analyzed, and the corresponding weights of each scoring indicator are preset; List at least one frequency band combination and exclude combinations containing interfering frequency bands. Score and rank each frequency band combination according to a frequency band scoring model. Evaluate the synergistic effects caused by interference between frequency bands and the performance improvement during parallel transmission, and select the combination with the best overall performance.

3. The high-definition video transmission optimization and encryption method based on the wifi7 chip module according to claim 2 is characterized in that: Dynamically binding at least one channel according to the network environment, adjusting the modulation mode according to the signal quality, and allocating network resources specifically include: Measure the received signal strength indicator of each channel to evaluate signal coverage and quality; Based on changes in the network environment, terminal devices are dynamically bound to the optimal channel or backup channel through software configuration; Count the bit error rate and packet loss rate during transmission, and set threshold ranges for the bit error rate and packet loss rate indicators based on historical data; Classifying signal quality according to the set threshold range, wherein the signal quality includes excellent, good, fair, and poor; Based on the signal quality level, select the modulation method that matches it; When the signal quality is excellent, select 64-order quadrature amplitude modulation to increase the transmission rate; When the signal quality is good, select 16-order quadrature amplitude modulation to improve noise resistance; When the signal quality is average, select quadrature phase shift keying to reduce the difficulty of data transmission; When the signal quality is poor, binary phase shift keying is selected to improve transmission reliability; Collect bandwidth and latency requirements of each user or device, and count the total load on the current network, the load of each channel, and the number of device connections; Based on the importance and bandwidth requirements of users or devices, set at least one priority level to give priority to meeting the resource needs of high-priority users or devices; Dynamically adjust resource allocation strategies based on changes in network load and user demand.

4. The high-definition video transmission optimization and encryption method based on the wifi7 chip module according to claim 3 is characterized in that: The establishment of a load prediction model, predicting load changes by real-time monitoring of network load, and adjusting resource allocation strategies in advance specifically include: Collect historical network load data and record timestamps for each piece of data; Remove outliers, missing values, and duplicate data from the load data, and normalize the remaining data to obtain the load data set; Constructing an autoregressive differential moving average model for load forecasting, wherein the autoregressive differential moving average model includes an autoregressive model part, a differential process part, and a moving average model part; The autoregressive model part eliminates the influence of observations of at least one period in the past on the current value by processing the autoregressive part of the time series; The difference process makes the non-stationary time series stationary through second-order difference processing, eliminating the trend and seasonal factors in the time series; The moving average model part eliminates the impact of past forecast errors on current values ​​by processing the moving average part of the time series; Divide the load data set into training set, validation set and test set, use the training set to train the model, adjust the model parameters to optimize the prediction performance, and use the validation set to verify the model and evaluate the generalization ability of the model; Use the trained load forecasting model to predict real-time data and obtain the load change trend for at least a period of time in the future; Based on the network load prediction results, set the threshold range for resource allocation; Develop at least one resource allocation strategy under load conditions, increase bandwidth, adjust the number of device connections, and optimize traffic routing.

5. The high-definition video transmission optimization and encryption method based on the wifi7 chip module according to claim 4 is characterized in that: Dynamically adjusting the preamble puncturing mode based on the interference situation to shield the interfered frequency band and retain the available frequency band specifically includes: Real-time collection of interference intensity, interference frequency range and interference duration data for each frequency band; Identify interference source types through signal feature analysis, including co-channel interference, adjacent-channel interference, and external device interference; Evaluate the interference level of each frequency band based on interference intensity, duration, and impact range; Setting the puncture position corresponding to the interfered frequency band in the preamble to shield all signals on the interfered frequency band, and identifying the available frequency band based on the interference analysis results; The puncturing pattern is adjusted to retain the preamble information and communication resources on the available frequency band and allocate them to at least one user or device according to actual needs.

6. The high-definition video transmission optimization and encryption method based on the wifi7 chip module according to claim 5 is characterized in that: The dynamic key is generated by the hardware security module and distributed to the terminal device through a secure channel. The key lifecycle management system is established to manage the generation, distribution, use and destruction of the key, including: Deploy a hardware security module to prevent unauthorized access, initialize and configure the hardware security module, and set the administrator password and security policy; Based on security standards and business requirements, select the key length to be generated and define the conditions and frequency for key generation; The hardware security module uses an internal true random number generator to generate key material and a key derivation function to derive the final key from the key material; Select the Secure Sockets Layer (SSL) cryptographic communication framework (CFC) encryption channel as the secure channel for key distribution and configure digital certificates for both ends of the channel. Configure security parameters for the encryption channel, including encryption algorithm, key length, and session timeout; Encapsulate the key in a key container and add metadata to the key container, wherein the metadata includes the key ID, generation time, and validity period; Select a symmetric encryption algorithm to encrypt the key container and use a hardware security module to manage the encryption key; The terminal device initiates a key distribution request through the client of the key management system, and the key management system verifies the distribution request; The key management system transmits the encrypted key container to the terminal device through a secure channel, monitors the key transmission process in real time, and records the transmission log; After receiving the key container, the terminal device performs integrity verification and decrypts the key container using the pre-configured decryption key to obtain the key; When encryption and decryption operations are required, the terminal device obtains the key from the storage environment and uses the corresponding encryption algorithm to perform the operation; When the key reaches the preset validity period, the destruction process is automatically triggered, and the authorized administrator initiates a key destruction request, verifies the destruction request, and locates the key that needs to be destroyed.

7. The high-definition video transmission optimization and encryption method based on the wifi7 chip module according to claim 6 is characterized in that: The use of a zero-trust architecture to continuously authenticate and authorize devices and users, combined with identity authentication methods, and optimized end-to-end encryption performance through hardware acceleration specifically includes: Based on the principle of least privilege, ensure that each user and device has only the minimum permissions required to complete their tasks. Based on the principle of continuous verification, perform continuous identity authentication and authorization checks on each access request. Based on business logic and security requirements, the network is divided into three micro-segments: application layer, data layer, and management layer. Each micro-segment maintains security isolation and prohibits lateral movement within the network. The first factor of user authentication is recorded as username and password, and the password is stored using an encrypted hash algorithm; The second factor of user authentication is recorded as dynamic token and biometric identification. Dynamic token is generated using hardware token, and the user enters the token value for verification. Biometric identification technologies such as fingerprint and facial recognition are also supported. The third factor of user authentication is recorded as a user behavior model, which records the user's normal behavior pattern; Collect user information context, device information context, and environmental information context. The user information context includes user identity, role, and permission information; the device information context includes device type, status, and location information; and the environmental information context includes access time, location, and network status information. The policy decision point makes authorization decisions based on the collected context information, performs risk assessment on access requests, and dynamically adjusts authorization decisions; The policy execution point executes the access control policy based on the authorization decision of the policy decision point, allowing or denying the access request; By utilizing the parallel computing capability of the hardware accelerator, at least one encryption and decryption task can be processed simultaneously to improve the task processing speed.

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