Wireless screen projection management system and method based on Internet of Things technology

Abstract

The invention discloses a wireless screen projection management system and method based on the Internet of Things technology, and belongs to the technical field of the Internet of Things. In the system, equipment is registered through an Internet of Things platform, the Internet of Things is used for automatic equipment discovery and automatic equipment broadcasting, and a control center identifies and manages newly-added equipment; a user initiates a wireless screen projection request, and the control center authorizes and manages the request based on a preset strategy; a data compression algorithm is adopted to reduce transmission data volume; analyzing the network condition in real time by using a deep Q network algorithm, and dynamically adjusting a data transmission strategy; recording screen projection quality parameters, analyzing historical screen projection data by using an artificial intelligence algorithm, predicting potential problems, providing optimization suggestions, and dynamically allocating and adjusting network bandwidth and equipment resources; collaboration among the equipment is realized by using the Internet of Things; faults occurring in the screen projection process are monitored in real time, a decision tree algorithm is used for analyzing fault causes, and recovery measures are automatically taken.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a wireless screen projection management system and method based on Internet of Things technology. Background Art

[0002] With the popularity of smart devices and multimedia applications, wireless projection technology has been widely used in many fields such as education, business and home entertainment. Wireless projection technology allows users to wirelessly transmit content from mobile phones, tablets or computers to large-screen display devices such as TVs and projectors. This technology greatly improves the interoperability and user experience between devices. However, the existing wireless projection technology still has many problems in practical applications: Wireless screen projection usually requires high network bandwidth support, and existing technologies often cannot guarantee the smoothness and clarity of the video when the network bandwidth is limited. Data transmission in a wireless environment is easily interfered with, resulting in problems such as video freezes, delays, or disconnections, affecting the user experience. Most existing technologies rely on manual operation and settings by users, lack intelligent automatic management and optimization mechanisms, and are difficult to cope with complex and changing network environments. Security issues in the data transmission process have not received sufficient attention, and there may be risks of data leakage or malicious attacks. Summary of the invention

[0003] The purpose of the present invention is to provide a wireless screen projection management system and method based on Internet of Things technology to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: A wireless screen projection management method based on Internet of Things technology, the method comprising the following steps: S100, the device is registered through the IoT platform, obtains a unique device ID, and uses device certificates and user credentials; the IoT is used for automatic device discovery, and the device automatically broadcasts its presence when entering the network. The control center identifies and manages newly added devices by listening to the broadcast; S200, the user initiates a wireless screen projection request through the application, and the control center authorizes and manages the request based on the preset strategy; uses a data compression algorithm to reduce the amount of transmitted data; uses a deep Q network algorithm to analyze the network status in real time and dynamically adjust the data transmission strategy; S300, real-time monitoring of screen projection data transmission, recording of screen projection quality parameters, using artificial intelligence algorithms to analyze historical screen projection data, predicting potential problems and providing optimization suggestions; the control center dynamically allocates and adjusts network bandwidth and device resources according to screen projection needs and network conditions; uses the Internet of Things to achieve collaboration between devices and optimize resource utilization; S400, real-time monitoring of faults occurring during screen projection, use of decision tree algorithm to analyze fault causes, and automatic adoption of recovery measures; when no recovery measures can be taken due to the actual environment, the user inputs the priority of various parameters to retain stable configuration when wireless screen projection is performed, and a greedy algorithm is used based on the priority to reallocate network bandwidth and device resources while ensuring user needs.

[0005] According to step S100, during the device manufacturing process, a unique device identification Device ID and a device certificate are pre-embedded, and the device certificate is a digital certificate issued by the device manufacturer; when the device is started for the first time, a registration request is sent to the Internet of Things platform, and the registration request includes the device identification, device certificate, public key, manufacturer information, geographic location, network information, timestamp, device capability description and device status information; the Internet of Things platform verifies the authenticity and validity of the device certificate, and uses the public key infrastructure PKI to verify the device certificate signature to ensure that the certificate has not been tampered with; after successful registration, the Internet of Things platform generates a device credential for the device, and the device credential is used for identity authentication and authorization in subsequent communications of the device.

[0006] According to step S100, each time the device communicates with the IoT platform, a device certificate is attached as proof of the device's identity. When a user initiates a screen projection request to the IoT platform through an application, the user needs to provide user credentials; the device and the user authenticate the IoT platform through their respective certificates and credentials, and the IoT platform verifies the validity of the device certificate and user credentials. After the dual authentication is passed, the device and the user obtain corresponding access rights; When a device enters the network, it automatically broadcasts its existence through the broadcast protocol mDNS. The control center discovers newly added devices in real time by listening to broadcast messages in the network. The control center verifies the legitimacy and identity of the device based on the device identification and broadcast content. The control center records the information of the new device and adds it to the device management list. The control center manages and allocates the new device according to the preset strategy.

[0007] According to step S200, the user selects a target device for screen projection in the application, the user enters the credential information, and the application sends a screen projection request to the control center through the secure channel HTTPS; after receiving the screen projection request, the control center parses the request content, which includes the user credential and the target device identifier; the control center verifies the validity of the user credential, ensures that the request comes from a legitimate user, and uses the identity authentication mechanism OAuth 2.0 to verify the user's identity; OAuth 2.0 is an open standard authorization protocol for third-party applications to access user resources without exposing the user's credentials to third parties. The following are the steps to verify user identity using OAuth 2.0: The user selects the target device for screen projection in the application and enters the credential information. The application sends a screen projection request to the control center through the secure channel HTTPS. The application redirects the user to the identity provider for authentication. The user enters the username and password on the identity provider's page to log in, and the user confirms that the application is authorized to access its resources.

[0008] After the identity provider verifies the user's identity, it returns an authorization code to the application. The application sends the authorization code to the control center, and the control center requests an access token from the identity provider. The control center uses the authorization code to request an access token from the token endpoint of the identity provider. After the identity provider verifies the authorization code, it returns the access token to the control center. The control center uses the access token to verify the user's identity to ensure that the request comes from a legitimate user. After the control center successfully verifies, it parses the content of the screen projection request, including the user credentials and the target device identifier. The control center verifies the validity of the user's credentials to ensure that the request comes from a legitimate user. The control center authorizes and manages screen projection requests based on preset policies.

[0009] The control center reviews the request based on the preset policy and decides whether to authorize the request based on the policy. The control center notifies the application of the authorization result. If the authorization is successful, the session ID and related configuration information are returned. Use H.264 / H.265 video compression to reduce the amount of video data, and dynamically adjust compression parameters according to network conditions and device performance; monitor network bandwidth and latency in real time, dynamically adjust data transmission strategies, and use adaptive bit rate streaming ABR to automatically adjust video quality according to network conditions; use RTP real-time transport protocol for video data transmission to ensure data real-time and low latency; use TLS / DTLS protocol to ensure the security of transmitted data.

[0010] According to step S200, the state space is used to represent the real-time status of the network and the current transmission configuration of the system. The state variables in the state space include: network bandwidth, network delay, data packet loss rate, video resolution and video bit rate; the action space is used to represent the adjustment strategy taken under a given state. The actions in the action space include: increasing or decreasing the video bit rate, changing the video resolution, and changing the video compression parameters; the actions are represented as discrete or continuous sets; When increasing or decreasing the video bitrate, set the bitrate adjustment step size , each adjustment increases or decreases the bit rate by one step; when changing the video resolution, set the video resolution supported by the application as a discretized action set; when changing the video compression parameters, set the step size of the compression parameter adjustment , each adjustment increases or decreases the compression parameter by one step; Initialize the Q network and the target network, and use the experience replay buffer D to store the experience samples ,in, is the current status, including network bandwidth , network delay , Packet loss rate , Video resolution and video bitrate ; Indicates in status The following actions are taken, Indicates in status Take Action The reward after the transmission reflects the quality of the transmission. Indicates the next state after executing the action; At each time step t, choose the action , and update the Q value based on the experience sample: ; in, is the learning rate, which indicates the step size of Q value update, is the discount factor, which represents the discount of future rewards, is the predicted action to be taken, is the weight of the Q network, are the weights of the target network.

[0011] During the training of the Deep Q-Network, we use -The greedy strategy selects actions to balance exploration and exploitation; when the probability is When the probability is hour, ,in, To explore the probability, it gradually decreases as the training progresses; Defining the reward function , used to measure the quality of transmission strategy: ; in, is the video quality score, based on the objective quality indicator PSNR, yes The weight parameter, yes The weight parameter, yes The weight parameter of Randomly extract several samples from the experience replay buffer D and update the Q network parameters; use the mean square error as the loss function; use the gradient descent method to minimize the loss function and update ; Regularly update the Q network Copy to the target network .

[0012] According to step S300, the system continuously collects real-time data related to screen projection through the Internet of Things during the screen projection process, and sends the real-time data to the control center for monitoring and analysis; the real-time monitoring data is stored in the database to form a historical screen projection data record; the time series analysis algorithm is used to analyze the data to detect the trend and periodic changes of the screen projection quality parameters; the anomaly detection algorithm is used to identify abnormal situations; the long short-term memory network LSTM is used to predict future screen projection quality parameters; based on the analysis of historical data and model prediction, the system can identify problems such as insufficient network bandwidth, excessive latency and increased packet loss rate; based on the prediction results, the system generates specific optimization suggestions.

[0013] Use a fixed-length time window to organize the data, such as the data of every second in the past 10 minutes as an input sequence. Build a model with one or more LSTM layers to learn the characteristics and trends of the time series. Add a fully connected layer after the LSTM layer to map the output of the LSTM layer to the predicted projection quality parameters. Use a part of the historical projection data records as the training dataset to ensure that the dataset contains various projection situations and network environments. Select the mean square error MSE and optimizer Adam to train the model. Train the model through multiple rounds of iterations so that it can accurately predict future projection quality parameters.

[0014] The real-time collected projection data is input into the trained LSTM model, which outputs the predicted future projection quality parameters, including bandwidth, latency, packet loss rate, etc. Based on the prediction results of the LSTM model, the trend and change of the projection quality parameters are analyzed to detect problems such as insufficient network bandwidth, high latency, and increased packet loss rate that appear in the prediction.

[0015] According to step S300, the control center evaluates the available bandwidth according to the current network status to ensure that higher priority tasks have sufficient bandwidth support; according to the screen projection requirements and network evaluation results, the control center dynamically allocates bandwidth, and in the case of insufficient bandwidth, adjusts the video bit rate or resolution to adapt to the current network conditions; during the screen projection process, if the network status changes, the control center will adjust the bandwidth allocation in real time; the control center evaluates the performance and resource usage of each screen projection device, and according to the screen projection requirements and device performance, the control center dynamically adjusts the computing resources of the device, and optimizes the use of device resources through genetic algorithms; Through the Internet of Things, communication and collaboration between devices are achieved. The control center coordinates several devices to complete the screen projection task together. The control center assigns tasks to different devices based on the real-time status and screen projection requirements of the devices. The devices cooperate with each other to share computing resources and network bandwidth.

[0016] According to step S400, during the screen projection process, the system monitors parameters in real time, and detects abnormal conditions through preset thresholds and rules; uses a decision tree algorithm to analyze the cause of the fault, classify the fault, and identify the fault type, which includes network faults, equipment performance problems, and software errors; based on historical fault data and monitoring data, trains a machine learning model so that it can accurately identify and classify faults; when a fault occurs, the system calls the trained model to analyze real-time data and determine the cause of the fault; according to different types of faults, preset corresponding recovery strategies; the system automatically executes the corresponding recovery script, and while taking recovery measures, notifies the user of the fault situation and the measures taken through the user application; When a user enters a wireless screen projection request in the application, the state variable data in the state space needs to be provided and the priority is set. The parameters are sorted according to the priority provided by the user. The system makes initial resource allocation based on the current network bandwidth and device resources. It tries to meet the highest priority demand. If the resources are insufficient, the parameter requirements are gradually reduced according to the priority. Redistribute based on the priority greedy algorithm, select the highest priority parameter from the priority list, check whether the current network bandwidth and device resources meet the requirements of the highest priority parameter, if the resources are sufficient, allocate resources to meet the parameter requirements, and record the current allocation status, if the resources are insufficient, gradually reduce the parameter requirements according to the resource situation until the resources can meet the adjusted requirements; for the next priority parameter, repeat the resource check and adjustment until all parameters are processed; during the screen projection process, the system continuously monitors the network and device resource status, if the resource status changes, the system uses the greedy algorithm to readjust the resource allocation of each parameter based on the real-time monitoring data and priority, to ensure that the high priority parameters are always met first, and reduce the configuration of the low priority parameters when the high priority parameters cannot be met; The system notifies the user of the current projection configuration and any adjustments through the application, provides user options, and allows the user to readjust priority settings when necessary. The system reallocates resources based on the new priority.

[0017] Extract key features from fault data, such as network bandwidth, latency, packet loss rate, CPU and memory usage, software error codes, etc. Select features that are highly relevant to fault classification to improve the classification performance of the model. Use labeled historical fault data to train the decision tree model, and the model learns how to classify faults into different types based on input features. When the system detects a fault, collect feature data when the fault occurs. Input real-time feature data into the trained decision tree model, and the model outputs the fault type.

[0018] A wireless screen projection management system based on Internet of Things technology, comprising: Device registration and discovery module: including: device registration unit and automatic device discovery unit; the device registration unit is responsible for registering the device through the IoT platform, obtaining a unique device ID, using device certificates and user credentials, and the automatic device discovery unit uses the IoT to perform automatic device discovery. When a device enters the network, it automatically broadcasts its existence. The control center identifies and manages newly added devices by monitoring the broadcast; Screen projection request management module: including: screen projection request unit, authorization management unit, data compression unit and network status analysis unit; wherein, the screen projection request unit is responsible for responding to the wireless screen projection request initiated by the user through the application, the authorization management unit is responsible for the control center to authorize and manage the request based on the preset strategy, the data compression unit adopts the data compression algorithm to reduce the amount of transmitted data, and the network status analysis unit uses the deep Q network algorithm to analyze the network status in real time and dynamically adjust the data transmission strategy; Data transmission monitoring and optimization module: including: transmission monitoring unit, historical data analysis unit, resource dynamic adjustment unit and device coordination unit; among them, the transmission monitoring unit monitors the screen projection data transmission in real time and records the screen projection quality parameters; the historical data analysis unit uses artificial intelligence algorithms to analyze historical screen projection data, predict potential problems and provide optimization suggestions; the resource dynamic adjustment unit is responsible for the control center to dynamically allocate and adjust network bandwidth and device resources according to screen projection requirements and network conditions; the device coordination unit uses the Internet of Things to achieve coordination between devices and optimize resource utilization; Fault monitoring and recovery module: includes: fault detection unit, fault cause analysis unit and automatic recovery unit; among them, the fault detection unit monitors the faults occurring during the screen projection process in real time, the fault cause analysis unit uses a decision tree algorithm to analyze the fault causes, and the automatic recovery unit is responsible for automatically taking recovery measures.

[0019] Compared with the prior art, the beneficial effects achieved by the present invention are: The present invention adopts a data compression algorithm to reduce the amount of transmitted data and improve transmission efficiency; uses a deep Q network algorithm to analyze network conditions in real time and dynamically adjust the data transmission strategy to ensure the quality of screen projection even when the network bandwidth is limited.

[0020] The present invention monitors the transmission of screen projection data in real time, records the quality parameters of screen projection, uses artificial intelligence algorithms to analyze historical screen projection data, predicts potential problems and provides optimization suggestions to ensure the stability of data transmission.

[0021] The control center of the present invention dynamically allocates and adjusts network bandwidth and device resources according to screen projection requirements and network conditions; realizes intelligent collaboration between devices through Internet of Things technology, optimizes resource utilization, reduces user manual intervention, and improves the automation and intelligence level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a schematic diagram of the steps of a wireless screen projection management method based on Internet of Things technology of the present invention; Figure 2 It is a system structure diagram of a wireless screen projection management system based on Internet of Things technology of the present invention. DETAILED DESCRIPTION

[0023] 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.

[0024] See also Figure 1 and Figure 2 , the present invention provides a technical solution: According to one embodiment of the present invention, Figure 1 A step diagram of a wireless screen projection management method based on the Internet of Things technology is shown in the figure, a wireless screen projection management method based on the Internet of Things technology, the method comprises the following steps: S100, the device is registered through the IoT platform, obtains a unique device ID, and uses device certificates and user credentials; the IoT is used for automatic device discovery, and the device automatically broadcasts its presence when entering the network. The control center identifies and manages newly added devices by listening to the broadcast; S200, the user initiates a wireless screen projection request through the application, and the control center authorizes and manages the request based on the preset strategy; uses a data compression algorithm to reduce the amount of transmitted data; uses a deep Q network algorithm to analyze the network status in real time and dynamically adjust the data transmission strategy; S300, real-time monitoring of screen projection data transmission, recording of screen projection quality parameters, using artificial intelligence algorithms to analyze historical screen projection data, predicting potential problems and providing optimization suggestions; the control center dynamically allocates and adjusts network bandwidth and device resources according to screen projection needs and network conditions; uses the Internet of Things to achieve collaboration between devices and optimize resource utilization; S400, real-time monitoring of faults occurring during screen projection, use of decision tree algorithm to analyze fault causes, and automatic adoption of recovery measures; when no recovery measures can be taken due to the actual environment, the user inputs the priority of various parameters to retain stable configuration when wireless screen projection is performed, and a greedy algorithm is used based on the priority to reallocate network bandwidth and device resources while ensuring user needs.

[0025] According to step S100, during the device manufacturing process, a unique device identification Device ID and a device certificate are pre-embedded, and the device certificate is a digital certificate issued by the device manufacturer; when the device is started for the first time, a registration request is sent to the Internet of Things platform, and the registration request includes the device identification, device certificate, public key, manufacturer information, geographic location, network information, timestamp, device capability description and device status information; the Internet of Things platform verifies the authenticity and validity of the device certificate, and uses the public key infrastructure PKI to verify the device certificate signature to ensure that the certificate has not been tampered with; after successful registration, the Internet of Things platform generates a device credential for the device, and the device credential is used for identity authentication and authorization in subsequent communications of the device.

[0026] According to step S100, each time the device communicates with the IoT platform, a device certificate is attached as proof of the device's identity. When a user initiates a screen projection request to the IoT platform through an application, the user needs to provide user credentials; the device and the user authenticate the IoT platform through their respective certificates and credentials, and the IoT platform verifies the validity of the device certificate and user credentials. After the dual authentication is passed, the device and the user obtain corresponding access rights; When a device enters the network, it automatically broadcasts its existence through the broadcast protocol mDNS. The control center discovers newly added devices in real time by listening to broadcast messages in the network. The control center verifies the legitimacy and identity of the device based on the device identification and broadcast content. The control center records the information of the new device and adds it to the device management list. The control center manages and allocates the new device according to the preset strategy.

[0027] According to step S200, the user selects a target device for screen projection in the application, the user enters the credential information, and the application sends a screen projection request to the control center through the secure channel HTTPS; after receiving the screen projection request, the control center parses the request content, which includes the user credential and the target device identifier; the control center verifies the validity of the user credential, ensures that the request comes from a legitimate user, and uses the identity authentication mechanism OAuth 2.0 to verify the user's identity; Use H.264 / H.265 video compression to reduce the amount of video data, and dynamically adjust compression parameters according to network conditions and device performance; monitor network bandwidth and latency in real time, dynamically adjust data transmission strategies, and use adaptive bit rate streaming ABR to automatically adjust video quality according to network conditions; use RTP real-time transport protocol for video data transmission to ensure data real-time and low latency; use TLS / DTLS protocol to ensure the security of transmitted data.

[0028] According to step S200, the state space is used to represent the real-time status of the network and the current transmission configuration of the system. The state variables in the state space include: network bandwidth, network delay, data packet loss rate, video resolution and video bit rate; the action space is used to represent the adjustment strategy taken under a given state. The actions in the action space include: increasing or decreasing the video bit rate, changing the video resolution, and changing the video compression parameters; the actions are represented as discrete or continuous sets; When increasing or decreasing the video bitrate, set the bitrate adjustment step size , each adjustment increases or decreases the bit rate by one step; when changing the video resolution, set the video resolution supported by the application as a discretized action set; when changing the video compression parameters, set the step size of the compression parameter adjustment , each adjustment increases or decreases the compression parameter by one step; Initialize the Q network and the target network, and use the experience replay buffer D to store the experience samples ,in, is the current status, including network bandwidth , network delay , Packet loss rate , Video resolution and video bitrate ; Indicates in status The following actions are taken, Indicates in status Take Action The reward after the transmission reflects the quality of the transmission. Indicates the next state after executing the action; According to this embodiment, in the initial state, the network bandwidth , network delay , packet loss rate , video resolution , video bitrate ; In action space, bitrate adjustment: , resolution adjustment: , compression parameter adjustment: .

[0029] At each time step t, choose the action , and update the Q value based on the experience sample: ; in, is the learning rate, which indicates the step size of Q value update, is the discount factor, which represents the discount of future rewards, is the predicted action to be taken, is the weight of the Q network, are the weights of the target network.

[0030] During the training of the Deep Q-Network, we use -The greedy strategy selects actions to balance exploration and exploitation; when the probability is When the probability is hour, ,in, To explore the probability, it gradually decreases as the training progresses; Defining the reward function , used to measure the quality of transmission strategy: ; in, is the video quality score, based on the objective quality indicator PSNR, yes The weight parameter, yes The weight parameter, yes The weight parameter of Randomly extract several samples from the experience replay buffer D and update the Q network parameters; use the mean square error as the loss function; use the gradient descent method to minimize the loss function and update ; Regularly update the Q network Copy to the target network .

[0031] By simulating a series of time steps, we obtain the following experimental data and results. Some of the results are as follows: Time step 1: Current state , select action: reduce bitrate by 10%, new state ,award ; Time step 2: current state , select action: Change video resolution to 1080p, new state ,award ; By training and updating the Q network multiple times, the system will gradually learn the optimal transmission strategy to maintain the best screen projection quality under different network conditions.

[0032] According to step S300, the system continuously collects real-time data related to screen projection through the Internet of Things during the screen projection process, and sends the real-time data to the control center for monitoring and analysis; the real-time monitoring data is stored in the database to form a historical screen projection data record; the time series analysis algorithm is used to analyze the data to detect the trend and periodic changes of the screen projection quality parameters; the anomaly detection algorithm is used to identify abnormal situations; the long short-term memory network LSTM is used to predict future screen projection quality parameters; based on the analysis of historical data and model prediction, the system can identify problems such as insufficient network bandwidth, excessive latency and increased packet loss rate; based on the prediction results, the system generates specific optimization suggestions.

[0033] Use a fixed-length time window to organize the data, such as the data of every second in the past 10 minutes as an input sequence. Build a model with one or more LSTM layers to learn the characteristics and trends of the time series. Add a fully connected layer after the LSTM layer to map the output of the LSTM layer to the predicted projection quality parameters. Use a part of the historical projection data records as the training dataset to ensure that the dataset contains various projection situations and network environments. Select the mean square error MSE and optimizer Adam to train the model. Train the model through multiple rounds of iterations so that it can accurately predict future projection quality parameters.

[0034] The real-time collected projection data is input into the trained LSTM model, which outputs the predicted future projection quality parameters, including bandwidth, latency, packet loss rate, etc. Based on the prediction results of the LSTM model, the trend and change of the projection quality parameters are analyzed to detect problems such as insufficient network bandwidth, high latency, and increased packet loss rate that appear in the prediction.

[0035] According to step S300, the control center evaluates the available bandwidth according to the current network status to ensure that higher priority tasks have sufficient bandwidth support; according to the screen projection requirements and network evaluation results, the control center dynamically allocates bandwidth, and in the case of insufficient bandwidth, adjusts the video bit rate or resolution to adapt to the current network conditions; during the screen projection process, if the network status changes, the control center will adjust the bandwidth allocation in real time; the control center evaluates the performance and resource usage of each screen projection device, and according to the screen projection requirements and device performance, the control center dynamically adjusts the computing resources of the device, and optimizes the use of device resources through genetic algorithms; Through the Internet of Things, communication and collaboration between devices are achieved. The control center coordinates several devices to complete the screen projection task together. The control center assigns tasks to different devices based on the real-time status and screen projection requirements of the devices. The devices cooperate with each other to share computing resources and network bandwidth.

[0036] According to step S400, during the screen projection process, the system monitors parameters in real time, and detects abnormal conditions through preset thresholds and rules; uses a decision tree algorithm to analyze the cause of the fault, classify the fault, and identify the fault type, which includes network faults, equipment performance problems, and software errors; based on historical fault data and monitoring data, trains a machine learning model so that it can accurately identify and classify faults; when a fault occurs, the system calls the trained model to analyze real-time data and determine the cause of the fault; according to different types of faults, preset corresponding recovery strategies; the system automatically executes the corresponding recovery script, and while taking recovery measures, notifies the user of the fault situation and the measures taken through the user application; When a user enters a wireless screen projection request in the application, the state variable data in the state space needs to be provided and the priority is set. The parameters are sorted according to the priority provided by the user. The system makes initial resource allocation based on the current network bandwidth and device resources. It tries to meet the highest priority demand. If the resources are insufficient, the parameter requirements are gradually reduced according to the priority. Redistribute based on the priority greedy algorithm, select the highest priority parameter from the priority list, check whether the current network bandwidth and device resources meet the requirements of the highest priority parameter, if the resources are sufficient, allocate resources to meet the parameter requirements, and record the current allocation status, if the resources are insufficient, gradually reduce the parameter requirements according to the resource situation until the resources can meet the adjusted requirements; for the next priority parameter, repeat the resource check and adjustment until all parameters are processed; during the screen projection process, the system continuously monitors the network and device resource status, if the resource status changes, the system uses the greedy algorithm to readjust the resource allocation of each parameter based on the real-time monitoring data and priority, to ensure that the high priority parameters are always met first, and reduce the configuration of the low priority parameters when the high priority parameters cannot be met; The system notifies the user of the current projection configuration and any adjustments through the application, provides user options, and allows the user to readjust priority settings when necessary. The system reallocates resources based on the new priority.

[0037] Extract key features from fault data, such as network bandwidth, latency, packet loss rate, CPU and memory usage, software error codes, etc. Select features that are highly relevant to fault classification to improve the classification performance of the model. Use labeled historical fault data to train the decision tree model, and the model learns how to classify faults into different types based on input features. When the system detects a fault, collect feature data when the fault occurs. Input real-time feature data into the trained decision tree model, and the model outputs the fault type.

[0038] According to another embodiment of the present invention, Figure 2 As shown in the system structure diagram of a wireless screen projection management system based on the Internet of Things technology, a wireless screen projection management system based on the Internet of Things technology includes: Device registration and discovery module: including: device registration unit and automatic device discovery unit; the device registration unit is responsible for registering the device through the IoT platform, obtaining a unique device ID, using device certificates and user credentials, and the automatic device discovery unit uses the IoT to perform automatic device discovery. When a device enters the network, it automatically broadcasts its existence. The control center identifies and manages newly added devices by monitoring the broadcast; Screen projection request management module: including: screen projection request unit, authorization management unit, data compression unit and network status analysis unit; wherein, the screen projection request unit is responsible for responding to the wireless screen projection request initiated by the user through the application, the authorization management unit is responsible for the control center to authorize and manage the request based on the preset strategy, the data compression unit adopts the data compression algorithm to reduce the amount of transmitted data, and the network status analysis unit uses the deep Q network algorithm to analyze the network status in real time and dynamically adjust the data transmission strategy; Data transmission monitoring and optimization module: including: transmission monitoring unit, historical data analysis unit, resource dynamic adjustment unit and device coordination unit; among them, the transmission monitoring unit monitors the screen projection data transmission in real time and records the screen projection quality parameters; the historical data analysis unit uses artificial intelligence algorithms to analyze historical screen projection data, predict potential problems and provide optimization suggestions; the resource dynamic adjustment unit is responsible for the control center to dynamically allocate and adjust network bandwidth and device resources according to screen projection requirements and network conditions; the device coordination unit uses the Internet of Things to achieve coordination between devices and optimize resource utilization; In this embodiment, the data transmission monitoring and optimization module uses the long short-term memory network LSTM to predict the future projection quality parameters. Time step 1: current state , the LSTM model predicts that in the next 10 minutes, the network bandwidth will drop to 4Mbps, the delay will increase to 150ms, and the packet loss rate will increase to 0.02; the control center adjusts the bit rate to 2.5Mbps and the resolution to 480p based on the prediction results; the new state ; Transmission quality parameters: bandwidth decreased, latency increased, and packet loss rate increased, but after adjusting the bit rate and resolution, the projection quality remained stable.

[0039] Time step 2: current state , the LSTM model predicts that within the next 10 minutes, the network bandwidth will be restored to 5Mbps, the delay will drop to 80ms, and the packet loss rate will drop to 0.01; the control center adjusts the bit rate to 3Mbps and the resolution to 720p based on the prediction results; the new state ; Transmission quality parameters: bandwidth recovery, latency reduction, packet loss rate reduction, and screen projection quality improvement.

[0040] Fault monitoring and recovery module: includes: fault detection unit, fault cause analysis unit and automatic recovery unit; among them, the fault detection unit monitors the faults occurring during the screen projection process in real time, the fault cause analysis unit uses a decision tree algorithm to analyze the fault causes, and the automatic recovery unit is responsible for automatically taking recovery measures.

[0041] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0042] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A wireless screen projection management method based on Internet of Things technology, characterized in that: The method comprises the following steps: S100, the device is registered through the IoT platform, obtains a unique device ID, and uses device certificates and user credentials; the IoT is used for automatic device discovery, and the device automatically broadcasts its presence when entering the network. The control center identifies and manages newly added devices by listening to the broadcast; S200, the user initiates a wireless screen projection request through the application, and the control center authorizes and manages the request based on a preset strategy; a data compression algorithm is used to reduce the amount of transmitted data; Use deep Q network algorithm to analyze network status in real time and dynamically adjust data transmission strategy; S300, real-time monitoring of screen projection data transmission, recording screen projection quality parameters, using artificial intelligence algorithms to analyze historical screen projection data, predicting potential problems and providing optimization suggestions; the control center dynamically allocates and adjusts network bandwidth and device resources according to screen projection needs and network conditions; Use the Internet of Things to achieve collaboration between devices and optimize resource utilization; S400, real-time monitoring of faults occurring during screen projection, use of decision tree algorithm to analyze fault causes, and automatic adoption of recovery measures; when no recovery measures can be taken due to the actual environment, the user inputs the priority of various parameters to retain stable configuration when wireless screen projection is performed, and a greedy algorithm is used based on the priority to reallocate network bandwidth and device resources while ensuring user needs.

2. According to claim 1, a wireless screen projection management method based on Internet of Things technology is characterized in that: According to step S100, during the device manufacturing process, a unique device identification Device ID and a device certificate are pre-embedded, and the device certificate is a digital certificate issued by the device manufacturer; when the device is started for the first time, a registration request is sent to the Internet of Things platform, and the registration request includes the device identification, device certificate, public key, manufacturer information, geographic location, network information, timestamp, device capability description and device status information; the Internet of Things platform verifies the authenticity and validity of the device certificate, and uses the public key infrastructure PKI to verify the device certificate signature to ensure that the certificate has not been tampered with; after successful registration, the Internet of Things platform generates a device credential for the device, and the device credential is used for identity authentication and authorization in subsequent communications of the device.

3. According to claim 2, a wireless screen projection management method based on Internet of Things technology is characterized in that: According to step S100, each time the device communicates with the IoT platform, a device certificate is attached as proof of the device's identity. When a user initiates a screen projection request to the IoT platform through an application, the user needs to provide user credentials; the device and the user authenticate the IoT platform through their respective certificates and credentials, and the IoT platform verifies the validity of the device certificate and user credentials. After the dual authentication is passed, the device and the user obtain corresponding access rights; When a device enters the network, it automatically broadcasts its existence through the broadcast protocol mDNS. The control center discovers newly added devices in real time by listening to broadcast messages in the network. The control center verifies the legitimacy and identity of the device based on the device identification and broadcast content. The control center records the information of the new device and adds it to the device management list. The control center manages and allocates the new device according to the preset strategy.

4. According to claim 1, a wireless screen projection management method based on Internet of Things technology is characterized in that: According to step S200, the user selects a target device for screen projection in the application, the user enters the credential information, and the application sends a screen projection request to the control center through the secure channel HTTPS; after receiving the screen projection request, the control center parses the request content, which includes the user credential and the target device identifier; the control center verifies the validity of the user credential, ensures that the request comes from a legitimate user, and uses the identity authentication mechanism OAuth 2.0 to verify the user's identity; The control center reviews the request based on the preset policy and decides whether to authorize the request based on the policy. The control center notifies the application of the authorization result. If the authorization is successful, the session ID and related configuration information are returned. Use H.264 / H.265 video compression to reduce the amount of video data, and dynamically adjust compression parameters according to network conditions and device performance; monitor network bandwidth and latency in real time, dynamically adjust data transmission strategies, and use adaptive bit rate streaming ABR to automatically adjust video quality according to network conditions; Use RTP real-time transmission protocol for video data transmission to ensure data real-time and low latency; Use TLS / DTLS protocol to ensure the security of transmitted data.

5. According to claim 4, a wireless screen projection management method based on Internet of Things technology is characterized in that: According to step S200, the state space is used to represent the real-time status of the network and the current transmission configuration of the system. The state variables in the state space include: network bandwidth, network delay, data packet loss rate, video resolution and video bit rate; the action space is used to represent the adjustment strategy taken under a given state. The actions in the action space include: increasing or decreasing the video bit rate, changing the video resolution, and changing the video compression parameters; the actions are represented as discrete or continuous sets; When increasing or decreasing the video bitrate, set the bitrate adjustment step size , each adjustment increases or decreases the bit rate by one step; when changing the video resolution, set the video resolution supported by the application as a discretized action set; when changing the video compression parameters, set the step size of the compression parameter adjustment , each adjustment increases or decreases the compression parameter by one step; Initialize the Q network and the target network, and use the experience replay buffer D to store the experience samples ,in, is the current status, including network bandwidth , network delay , Packet loss rate , Video resolution and video bitrate ; Indicates in status The following actions are taken, Indicates in status Take Action The reward after the transmission reflects the quality of the transmission. Indicates the next state after executing the action; At each time step t, choose the action , and update the Q value based on the experience sample: ； in, is the learning rate, which indicates the step size of Q value update, is the discount factor, which represents the discount of future rewards, is the predicted action to be taken, is the weight of the Q network, are the weights of the target network.

6. According to claim 4, a wireless screen projection management method based on Internet of Things technology is characterized in that: During the training of the Deep Q-Network, we use -The greedy strategy selects actions to balance exploration and exploitation; when the probability is When the probability is hour, ,in, To explore the probability, it gradually decreases as the training progresses; Defining the reward function , used to measure the quality of transmission strategy: ； in, is the video quality score, based on the objective quality indicator PSNR, yes The weight parameter, yes The weight parameter, yes The weight parameter of Randomly extract several samples from the experience replay buffer D and update the Q network parameters; use the mean square error as the loss function; use the gradient descent method to minimize the loss function and update ; Regularly update the Q network Copy to the target network .

7. According to claim 1, a wireless screen projection management method based on Internet of Things technology is characterized in that: According to step S300, the system continuously collects real-time data related to screen projection through the Internet of Things during the screen projection process, and sends the real-time data to the control center for monitoring and analysis; stores the real-time monitored data in the database to form a historical screen projection data record; uses a time series analysis algorithm to perform data analysis to detect trends and periodic changes in screen projection quality parameters; and uses an anomaly detection algorithm to identify abnormal situations; The long short-term memory network (LSTM) is used to predict future projection quality parameters. Based on the analysis of historical data and model prediction, the system can identify problems such as insufficient network bandwidth, excessive latency, and increased packet loss rate. Based on the prediction results, the system generates specific optimization suggestions.

8. According to claim 7, a wireless screen projection management method based on Internet of Things technology is characterized in that: According to step S300, the control center evaluates the available bandwidth according to the current network status to ensure that higher priority tasks have sufficient bandwidth support; according to the screen projection requirements and network evaluation results, the control center dynamically allocates bandwidth, and in the case of insufficient bandwidth, adjusts the video bit rate or resolution to adapt to the current network conditions; during the screen projection process, if the network status changes, the control center will adjust the bandwidth allocation in real time; the control center evaluates the performance and resource usage of each screen projection device, and according to the screen projection requirements and device performance, the control center dynamically adjusts the computing resources of the device, and optimizes the use of device resources through genetic algorithms; Through the Internet of Things, communication and collaboration between devices are achieved. The control center coordinates several devices to complete the screen projection task together. The control center assigns tasks to different devices based on the real-time status and screen projection requirements of the devices. The devices cooperate with each other to share computing resources and network bandwidth.

9. According to claim 1, a wireless screen projection management method based on Internet of Things technology is characterized in that: According to step S400, during the screen projection process, the system monitors parameters in real time and detects abnormal conditions through preset thresholds and rules; uses a decision tree algorithm to analyze the causes of faults, classify faults, and identify fault types, which include network faults, device performance issues, and software errors; Based on historical fault data and monitoring data, train machine learning models to enable them to accurately identify and classify faults; When a fault occurs, the system calls the trained model to analyze real-time data and determine the cause of the fault. According to different types of faults, the corresponding recovery strategy is preset. The system automatically executes the corresponding recovery script and notifies the user of the fault status and the measures taken through the user application while taking recovery measures. When a user enters a wireless screen projection request in the application, the state variable data in the state space needs to be provided and the priority is set. The parameters are sorted according to the priority provided by the user. The system makes initial resource allocation based on the current network bandwidth and device resources. It tries to meet the highest priority demand. If the resources are insufficient, the parameter requirements are gradually reduced according to the priority. Redistribute based on the priority greedy algorithm, select the highest priority parameter from the priority list, check whether the current network bandwidth and device resources meet the requirements of the highest priority parameter, if the resources are sufficient, allocate resources to meet the parameter requirements, and record the current allocation status, if the resources are insufficient, gradually reduce the parameter requirements according to the resource situation until the resources can meet the adjusted requirements; for the next priority parameter, repeat the resource check and adjustment until all parameters are processed; during the screen projection process, the system continuously monitors the network and device resource status, if the resource status changes, the system uses the greedy algorithm to readjust the resource allocation of each parameter based on the real-time monitoring data and priority, to ensure that the high priority parameters are always met first, and reduce the configuration of the low priority parameters when the high priority parameters cannot be met; The system notifies the user of the current projection configuration and any adjustments through the application, provides user options, and allows the user to readjust priority settings when necessary. The system reallocates resources based on the new priority.

10. A wireless screen projection management system based on Internet of Things technology, using a wireless screen projection management method based on Internet of Things technology as claimed in any one of claims 1 to 9, characterized in that: include: Device registration and discovery module: including: device registration unit and automatic device discovery unit; the device registration unit is responsible for registering the device through the IoT platform, obtaining a unique device ID, using device certificates and user credentials, and the automatic device discovery unit uses the IoT to perform automatic device discovery. When a device enters the network, it automatically broadcasts its existence. The control center identifies and manages newly added devices by monitoring the broadcast; Screen projection request management module: including: screen projection request unit, authorization management unit, data compression unit and network status analysis unit; wherein, the screen projection request unit is responsible for responding to the wireless screen projection request initiated by the user through the application, the authorization management unit is responsible for the control center to authorize and manage the request based on the preset strategy, the data compression unit adopts the data compression algorithm to reduce the amount of transmitted data, and the network status analysis unit uses the deep Q network algorithm to analyze the network status in real time and dynamically adjust the data transmission strategy; Data transmission monitoring and optimization module: including: transmission monitoring unit, historical data analysis unit, resource dynamic adjustment unit and device coordination unit; among them, the transmission monitoring unit monitors the screen projection data transmission in real time and records the screen projection quality parameters; the historical data analysis unit uses artificial intelligence algorithms to analyze historical screen projection data, predict potential problems and provide optimization suggestions; the resource dynamic adjustment unit is responsible for the control center to dynamically allocate and adjust network bandwidth and device resources according to screen projection requirements and network conditions; the device coordination unit uses the Internet of Things to achieve coordination between devices and optimize resource utilization; Fault monitoring and recovery module: includes: fault detection unit, fault cause analysis unit and automatic recovery unit; among them, the fault detection unit monitors the faults occurring during the screen projection process in real time, the fault cause analysis unit uses a decision tree algorithm to analyze the fault causes, and the automatic recovery unit is responsible for automatically taking recovery measures.

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