Signal transmission and encryption method and system for mobile handheld device in explosion-proof scene internet of things

Abstract

This invention belongs to the field of signal transmission technology and discloses a method and system for signal transmission and encryption of IoT mobile handheld devices in explosion-proof scenarios. The method includes the following steps: collecting real-time multi-dimensional status monitoring data of IoT mobile handheld devices in explosion-proof scenarios; obtaining the real-time health index of the real-time multi-dimensional status monitoring data; if the real-time health index exceeds a health index threshold, proceeding to the next step; performing status anomaly detection; optimizing signal transmission and encryption; switching the signal transmission channels of a multi-channel signal transmission fusion network; updating the dynamic encryption algorithm for the signal to be transmitted; encrypting the signal to be transmitted from the IoT mobile handheld device according to the real-time dynamic encryption algorithm, and transmitting the encrypted signal by updating the signal transmission channels. This invention solves the problems of unstable signal quality, low transmission security, and insufficient real-time performance in existing technologies.

Description

Technical Field

[0001] This invention belongs to the field of signal transmission technology, specifically relating to a method and system for signal transmission and encryption of IoT mobile handheld devices in explosion-proof scenarios. Background Technology

[0002] In explosion-proof environments such as petrochemical plants, coal mines, and gas stations, flammable and explosive gases, dust, and other hazardous substances are present, posing a risk of safety accidents. Therefore, it is necessary to use IoT mobile handheld devices for real-time signal transmission. Ensuring the stability, reliability, and security of signal transmission from these mobile handheld devices has become an important research direction in this field.

[0003] The existing technology has the following drawbacks:

[0004] 1) Unstable signal quality: Traditional signal transmission methods often rely on a single signal transmission channel and lack the ability to monitor and dynamically adjust signal quality in real time. In explosion-proof scenarios, due to environmental factors (such as electromagnetic interference, signal attenuation, etc.), signal quality is prone to fluctuation, which leads to reduced reliability of data transmission.

[0005] 2) Low transmission security; traditional encryption methods often use fixed encryption algorithms and keys, which are difficult to cope with dynamically changing security threats and pose significant security risks;

[0006] 3) Insufficient real-time performance: Traditional signal transmission and encryption methods have limitations in processing speed, making it difficult to meet the high real-time requirements of explosion-proof scenarios. Especially when the data volume is large or the network conditions are poor, it can easily lead to data transmission delays, affecting the system's response speed. Summary of the Invention

[0007] To address the problems of unstable signal quality, low transmission security, and insufficient real-time performance in existing technologies, this invention aims to provide a method and system for signal transmission and encryption of IoT mobile handheld devices in explosion-proof scenarios.

[0008] The technical solution adopted in this invention is as follows:

[0009] A method for signal transmission and encryption of IoT mobile handheld devices in explosion-proof scenarios includes the following steps:

[0010] Collect real-time multi-dimensional status monitoring data of the current signal transmission channel of IoT mobile handheld devices in explosion-proof scenarios in a multi-channel signal transmission fusion network;

[0011] The real-time health index of the real-time multidimensional status monitoring data is obtained using the health index dynamic calculation model. If the real-time health index exceeds the health index threshold, proceed to the next step.

[0012] Using a state anomaly detection data model, state anomalies are detected in real-time multidimensional state monitoring data to obtain real-time state anomaly detection results.

[0013] Based on the real-time anomaly detection results, a signal transmission and encryption optimization model is used to optimize signal transmission and encryption, resulting in a real-time signal transmission and encryption optimization scheme.

[0014] Based on the real-time signal transmission and encryption optimization scheme, the signal transmission link selection model is used to switch the signal transmission channels of the multi-channel signal transmission fusion network to obtain the updated signal transmission channels.

[0015] Based on the real-time signal transmission and encryption optimization scheme, an adaptive selection model of the encryption protocol is used to update the dynamic encryption algorithm of the signal to be transmitted and generate the corresponding updated dynamic encryption algorithm.

[0016] The signal to be transmitted from the IoT mobile handheld device is encrypted using a real-time dynamic encryption algorithm, and the encrypted signal is transmitted by updating the signal transmission channel.

[0017] Furthermore, the real-time multi-dimensional status monitoring data includes the real-time signal quality status of the signal to be transmitted, the real-time transmission network status of the current signal transmission channel, the real-time device status of the IoT mobile handheld device, and the real-time environmental status of the explosion-proof scenario.

[0018] Real-time signal quality status includes real-time signal loss rate, real-time noise level, real-time delay jitter, and real-time signal strength;

[0019] Real-time transmission network status includes real-time link bandwidth utilization, real-time packet loss rate, real-time round-trip time, real-time QoS level, and real-time backup network type and signal strength;

[0020] Real-time device status includes real-time battery level, real-time device temperature, real-time device vibration, and real-time device location;

[0021] Real-time environmental conditions include real-time temperature, real-time humidity, real-time air pressure, real-time combustible gas concentration, and real-time electromagnetic field intensity.

[0022] Furthermore, the formula for the dynamic calculation model of the health index is as follows: ;

[0023] In the formula, For real-time health index; Environmental correction factor; for The threshold baseline value for this data collection; for Multidimensional status monitoring data collected in this data acquisition; This is the minimum value of historical multidimensional status monitoring data; for The health index weights for each data collection; N is the total number of dimensions in the multidimensional status monitoring data. This indicates the number of times data was collected. For multidimensional status monitoring data dimensional indicators; for Quality factor of the second data acquisition; ;

[0024] In the formula, , for The threshold baseline value for this data collection; For smoothing coefficients; for Multidimensional status monitoring data collected in this data acquisition; ;

[0025] In the formula, for The weighting of the health index in this data collection; The compensation coefficient; The maximum value of historical multidimensional status monitoring data and the maximum value of health index weight; for This data collection involves multi-dimensional status monitoring data.

[0026] Furthermore, the state anomaly detection data model is constructed based on the LSTM-attention-MLP algorithm, and the state anomaly detection data model includes a data feature extraction module constructed based on the LSTM algorithm, an attention weight module constructed based on the attention mechanism, and a state anomaly detection module constructed based on the MLP algorithm, which are connected in sequence.

[0027] Furthermore, using a state anomaly detection data model, state anomaly detection is performed on real-time multidimensional state monitoring data to obtain real-time state anomaly detection results, including the following steps:

[0028] The real-time multidimensional status monitoring data is preprocessed to obtain preprocessed real-time multidimensional status monitoring data, which is then input into the status anomaly detection data model.

[0029] The data feature extraction module of the state anomaly detection data model is used to extract real-time data features of the preprocessed real-time multidimensional state monitoring data.

[0030] Based on the preset attention weight module, the attention weight module of the state anomaly detection data model is used to perform weighted fusion of several real-time data feature components to obtain real-time fused features.

[0031] Based on the real-time fusion characteristics, the state anomaly detection module of the state anomaly detection data model is used to perform state anomaly detection and obtain real-time state anomaly detection results.

[0032] Furthermore, the formula for anomaly detection is: ;

[0033] In the formula, This is the result of real-time anomaly detection; Real-time multidimensional status monitoring data extracted by the data feature extraction module The Middle Dimensional real-time data characteristics; Preset attention weight module; The anomaly detection prediction function is used to obtain the probability distribution. The predicted label with the highest probability obtained from state anomaly detection based on real-time fusion features is taken as the real-time state anomaly detection result. This represents the threshold for the health index.

[0034] Furthermore, the signal transmission and encryption optimization model is constructed based on the MOCPO algorithm, and the signal transmission and encryption optimization model includes an influence factor generation module, an optimization target update module, an initialization module, an iterative optimization module, and a vector decoding module connected in sequence.

[0035] Furthermore, the formula for the signal transmission link selection model is as follows: ;

[0036] In the formula, for The backup signal transmission channel selected for this data acquisition; It is a utility function; This refers to the parameters of the signal transmission channels in a multi-channel signal transmission fusion network. This refers to all signal transmission channels in a multi-channel signal transmission fusion network; This represents the total number of signal transmission channels. This is the result of real-time anomaly detection; ;

[0037] In the formula, for The update signal transmission channel for the next data acquisition; for The backup signal transmission channel selected for this data acquisition; for The original signal transmission channel for this data acquisition; Select trigger command for the real-time signal transmission link in the real-time signal transmission and encryption optimization scheme.

[0038] Furthermore, the formula for the adaptive selection model of the encryption protocol is: ;

[0039] In the formula, for The backup dynamic encryption algorithm selected for this data acquisition; Value function; The parameter is assigned to the dynamic encryption algorithm in the adaptive selection list for the encryption protocol. The encryption protocol adaptively selects all dynamic encryption algorithms from the list; This represents the total number of dynamic encryption algorithms. This is the result of real-time anomaly detection; ;

[0040] In the formula, for The updated dynamic encryption algorithm for each data acquisition; for The backup dynamic encryption algorithm selected for this data acquisition; for The original dynamic encryption algorithm for this data acquisition; The trigger command is adaptively selected for the real-time encryption protocol in the real-time signal transmission and encryption optimization scheme.

[0041] An explosion-proof IoT mobile handheld device signal transmission and encryption system is provided to realize the signal transmission and encryption method of the explosion-proof IoT mobile handheld device. The system includes a status monitoring data acquisition unit, a health index dynamic calculation unit, a status anomaly detection unit, a signal transmission and encryption optimization unit, a signal transmission link selection unit, an encryption protocol adaptive selection unit, and a signal transmission and encryption execution unit connected in sequence.

[0042] The beneficial effects of this invention are as follows: This invention discloses a method and system for signal transmission and encryption of IoT mobile handheld devices in explosion-proof scenarios. By collecting multi-dimensional status monitoring data in real time, including signal quality, network status, device status, and environmental status, it can comprehensively perceive the health status of the current signal transmission channel. Using a dynamic health index calculation model, it quantitatively evaluates the real-time multi-dimensional status monitoring data, promptly detects anomalies in the signal transmission channel, and intelligently switches to the optimal signal transmission channel based on the anomaly detection results through a multi-modal link selection model. This effectively avoids signal interruption caused by a single channel failure, significantly improving the reliability of signal transmission. For the safety requirements of explosion-proof scenarios, this invention adopts an adaptive encryption protocol selection model. Based on real-time anomaly detection results, the most suitable encryption algorithm is dynamically selected. This real-time dynamic encryption algorithm encrypts the signals to be transmitted from IoT mobile handheld devices, ensuring data security during transmission and effectively resisting dynamically changing security threats. Combined with an updated signal transmission channel, encrypted signal transmission further enhances data transmission security, meeting the high security standards required in explosion-proof scenarios. Through real-time monitoring and dynamic adjustment mechanisms, it can quickly respond to changes in signal transmission channels and network status, reducing data transmission latency. The introduction of a multimodal link selection model and an adaptive encryption protocol selection model optimizes the signal transmission path and encryption process, improving data transmission efficiency and meeting the high real-time requirements of explosion-proof scenarios.

[0043] Other beneficial effects of the present invention will be further explained in the specific embodiments. Attached Figure Description

[0044] Figure 1 This is a flowchart of the signal transmission and encryption method for IoT mobile handheld devices in explosion-proof scenarios according to the present invention.

[0045] Figure 2 This is a structural block diagram of the signal transmission and encryption system for IoT mobile handheld devices in explosion-proof scenarios in this invention. Detailed Implementation

[0046] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments.

[0047] Example 1:

[0048] like Figure 1 As shown, this embodiment provides a method for signal transmission and encryption of IoT mobile handheld devices in explosion-proof scenarios, including the following steps:

[0049] S1: Collect real-time multi-dimensional status monitoring data of the current signal transmission channel of IoT mobile handheld devices in explosion-proof scenarios in a multi-channel signal transmission fusion network;

[0050] Real-time multidimensional status monitoring data includes the real-time signal quality status of the signal to be transmitted, the real-time transmission network status of the current signal transmission channel, the real-time device status of IoT mobile handheld devices, and the real-time environmental status of the explosion-proof scenario.

[0051] Multimodal communication converged networks, including but not limited to:

[0052] Dual satellite and ground links: The system uses BeiDou satellite communication (supporting low-orbit satellite enhancement) as the main link and ground networks (4G / 5G, explosion-proof WiFi 6) as the secondary link to achieve seamless switching in complex environments. The satellite link can penetrate buildings or severe weather, while the ground network provides high bandwidth.

[0053] Real-time signal quality status includes real-time signal loss rate, real-time noise level, real-time delay jitter, and real-time signal strength;

[0054] Real-time transmission network status includes real-time link bandwidth utilization, real-time packet loss rate, real-time round-trip time, real-time Quality of Service (QoS) level, and real-time backup network type (such as Wi-Fi, cellular network, Bluetooth, etc.) and signal strength.

[0055] Real-time device status includes real-time battery level, real-time device temperature, real-time device vibration, and real-time device location of IoT mobile handheld devices, obtained through built-in sensors or external positioning systems.

[0056] Real-time environmental conditions include real-time temperature, real-time humidity, real-time air pressure, real-time combustible gas concentration, and real-time electromagnetic field strength, obtained through built-in or external sensors.

[0057] S2: Use the health index dynamic calculation model to obtain the real-time health index of the real-time multidimensional status monitoring data. If the real-time health index exceeds the health index threshold, proceed to the next step.

[0058] The formula for the dynamic calculation model of the health index is: ;

[0059] In the formula, For real-time health index; Environmental correction factor; for The threshold baseline value for this data collection; for Multidimensional status monitoring data collected in this data acquisition; This is the minimum value of historical multidimensional status monitoring data; for The health index weights for each data collection; N is the total number of dimensions in the multidimensional status monitoring data. This indicates the number of times data was collected. For multidimensional status monitoring data dimensional indicators; for Quality factor of the second data acquisition;

[0060] ;

[0061] In the formula, for The threshold baseline value for this data collection; For smoothing coefficients; for Multidimensional status monitoring data collected in this data acquisition;

[0062] ;

[0063] In the formula, for The weighting of the health index in this data collection; The compensation coefficient; The maximum value of historical multidimensional status monitoring data and the maximum value of health index weight; for Multidimensional status monitoring data collected in this data acquisition;

[0064] S3: Use the state anomaly detection data model to perform state anomaly detection on real-time multi-dimensional state monitoring data and obtain real-time state anomaly detection results;

[0065] The state anomaly detection data model is built based on the Long Short Term Memory (LSTM) network-attention-Multilayer Perceptron (MLP) algorithm. The state anomaly detection data model includes a data feature extraction module built based on the LSTM algorithm, an attention weight module built based on the attention mechanism, and a state anomaly detection module built based on the MLP algorithm, which are connected in sequence.

[0066] Using a state anomaly detection data model, state anomalies are detected in real-time multidimensional state monitoring data to obtain real-time state anomaly detection results. The process includes the following steps:

[0067] S3-1: Preprocess the real-time multidimensional status monitoring data to obtain preprocessed real-time multidimensional status monitoring data, and input it into the status anomaly detection data model;

[0068] S3-2: The data feature extraction module of the state anomaly detection data model is used to extract the real-time data features of the preprocessed real-time multidimensional state monitoring data.

[0069] S3-3: Based on the preset attention weight module, the attention weight module of the state anomaly detection data model is used to perform weighted fusion of several real-time data feature components to obtain real-time fused features.

[0070] S3-4: Based on the real-time fusion characteristics, use the state anomaly detection module of the state anomaly detection data model to perform state anomaly detection and obtain real-time state anomaly detection results;

[0071] The formula for detecting state anomalies is: ;

[0072] In the formula, This is the result of real-time anomaly detection; Real-time multidimensional status monitoring data extracted by the data feature extraction module The Middle Dimensional real-time data characteristics; Preset attention weight module; The anomaly detection prediction function is used to obtain the probability distribution. The predicted label with the highest probability obtained from state anomaly detection based on real-time fusion features is taken as the real-time state anomaly detection result. The threshold for the health index;

[0073] S4: Based on the real-time anomaly detection results, use the signal transmission and encryption optimization model to optimize signal transmission and encryption, and obtain a real-time signal transmission and encryption optimization scheme.

[0074] The signal transmission and encryption optimization model is constructed based on the Multi-objective Crested Porcupine Optimizer (MOCPO) algorithm, and the signal transmission and encryption optimization model includes an influence factor generation module, an optimization target update module, an initialization module, an iterative optimization module, and a vector decoding module connected in sequence.

[0075] Based on the real-time anomaly detection results, a signal transmission and encryption optimization model is used to optimize signal transmission and encryption, resulting in a real-time signal transmission and encryption optimization scheme, including the following steps:

[0076] S4-1: Based on the real-time anomaly detection results, use the influence factor generation module of the signal transmission and encryption optimization model to generate the corresponding real-time influence factor;

[0077] Simultaneously considering multiple influencing factors, such as signal transmission stability, transmission efficiency, integrity, encryption complexity, and encryption security, an iterative optimization process can find a balance among multiple objectives, generating signal transmission and encryption optimization schemes that meet various needs and adapt to diverse requirements of different transmission channels and signals to be transmitted. In this embodiment, real-time influencing factors include signal transmission cost, signal transmission loss rate, and encryption complexity.

[0078] S4-2: Based on the real-time impact factor, use the optimization target update module of the signal transmission and encryption optimization model to update the predicted optimization target, obtain the real-time optimization target, and set the real-time fitness function based on the real-time optimization target;

[0079] The formula is: ;

[0080] In the formula, This is the real-time fitness function; It is a signal transmission cost function; This is a function of signal transmission loss rate; For encryption complexity function; For MOCPO individuals; The first weight value, the second weight value, and the third weight value;

[0081] S4-3: Encode the initial real-time signal transmission and encryption optimization scheme into an individual vector of the initialization module, and use the initialization module of the signal transmission and encryption optimization model to generate several initial solutions (initial MOCPO individuals) based on the individual vectors, thus obtaining an initial MOCPO population composed of several initial MOCPO individuals.

[0082] The formula is: ;

[0083] In the formula, For the initial MOCPO individual of the Circle chaotic map, i.e., the initial solution; The initial MOCPO individuals are randomly generated; For MOCPO individual indicators; The modulo function is used for the initial population generation of the Circle chaotic mapping sequence. Compared with the randomly distributed population, the improved MOCPO individuals have a more uniform initial position distribution, which expands the search range of the algorithm in space, increases the diversity of the population position, and improves the algorithm's tendency to get trapped in local optima to a certain extent, thereby improving the algorithm's optimization efficiency.

[0084] S4-4: Based on the real-time fitness function, the iterative optimization module of the signal transmission and encryption optimization model is used to iteratively optimize several initial solutions to obtain the optimal solution, including the following steps:

[0085] S4-4-1: Set the MOCPO population parameters and the maximum number of iterations, introduce a cyclic population reduction mechanism, limit the number of individuals in the MOCPO population parameters, and obtain the updated MOCPO population parameters for the next iteration;

[0086] The formula is: ;

[0087] In the formula, For the first The number of individuals in the MOCPO population parameters of the next iteration; For the first The number of individuals in the MOCPO population parameters of the next iteration; This represents the minimum number of individuals in the MOCPO population parameters. Parameters for function evaluation; Evaluate the loop parameters for the function; The loop parameters are used to evaluate the maximum function; t is an indicator of the number of iterations.

[0088] S4-4-2: Calculate the initial fitness value of the initial MOCPO individuals in the initial MOCPO population based on the real-time fitness function.

[0089] S4-4-3: Based on the initial fitness value and the updated MOCPO population parameters, use the first defense strategy, the second defense strategy, the third defense strategy, and the fourth defense strategy to update the initial MOCPO population and obtain the updated MOCPO population.

[0090] The formula for the first defense strategy is: ;

[0091] In the formula, For updated MOCPO individuals within the first defense range; The initial MOCPO individuals within the first defensive range; These are random numbers based on a normal distribution. The value is a random value in the interval [0,1]. This is the optimal solution within the first defensive range; A vector generated between the true optimal solution within the first defense range and the optimal solution randomly selected from the MOCPO population; For MOCPO individual indicators; For iteration indication;

[0092] The formula for the second defense strategy is: ;

[0093] In the formula, For updated MOCPO individuals within the second defense range; The initial MOCPO individuals within the second defense range; This is the upper limit vector for the search of the second defense range; The value is a random value in the interval [0,1]. The first One initial MOCPO individual; All Two random integers between; The vector generated between the true optimal solution within the second defense perimeter and the optimal solution randomly selected from the MOCPO population;

[0094] The formula for the third defense strategy is: ;

[0095] In the formula, For updated MOCPO individuals within the third defense range; The initial MOCPO individuals within the third defense range; This is the upper limit vector for the search of the third defense range; The first One initial MOCPO individual; for Random integers between [a certain range]; Odor diffusion factor defined for the fitness function; As a defensive factor; This is a parameter for controlling the search direction;

[0096] The formula for the fourth defense strategy is: ;

[0097] In the formula, For updated MOCPO individuals within the fourth defense range; The initial MOCPO individuals within the fourth defense range; This is the optimal solution within the fourth defense range; All are random values ​​in the interval [0,1]. As a defensive factor; This is a parameter for controlling the search direction; The average force affecting the search direction; This is the convergence rate factor;

[0098] S4-4-4: Use a dynamic back-learning algorithm to perform dynamic back-learning on the updated MOCPO population to generate a dynamically back-learned MOCPO population.

[0099] The formula is: ;

[0100] In the formula, For dynamically reversed MOCPO individuals; The coefficient of inertia is decreasing; These are the maximum and minimum values ​​in the vector space, respectively. For updated MOCPO individuals;

[0101] S4-4-5: Based on the real-time fitness function, calculate the fitness value of all MOCPO individuals in the updated MOCPO population and the dynamically reversed MOCPO population, and select the MOCPO individual with the lowest fitness value as the optimal individual and retain the optimal individual.

[0102] S4-4-6: If the number of iterations for iterative optimization reaches the maximum number of iterations or the fitness value of the optimal individual meets the requirements, then the optimal solution corresponding to the optimal individual will be output.

[0103] S4-5: Using the vector decoding module of the signal transmission and encryption optimization model, decode the individual vectors of the optimal solution to obtain the optimal real-time signal transmission and encryption optimization scheme.

[0104] S5: Based on the real-time signal transmission and encryption optimization scheme, the signal transmission link selection model is used to switch the signal transmission channels of the multi-channel signal transmission fusion network to obtain updated signal transmission channels, such as switching from Wi-Fi to cellular network signal transmission channels, or selecting a signal transmission channel more suitable for explosion-proof scenarios.

[0105] The formula for the signal transmission link selection model is: ;

[0106] In the formula, for The backup signal transmission channel selected for this data acquisition; It is a utility function; This refers to the parameters of the signal transmission channels in a multi-channel signal transmission fusion network. This refers to all signal transmission channels in a multi-channel signal transmission fusion network; This represents the total number of signal transmission channels. This is the result of real-time anomaly detection;

[0107] ;

[0108] In the formula, for The update signal transmission channel for the next data acquisition; for The backup signal transmission channel selected for this data acquisition; for The original signal transmission channel for this data acquisition; Select trigger command for the real-time signal transmission link in the real-time signal transmission and encryption optimization scheme;

[0109] S6: Based on the real-time signal transmission and encryption optimization scheme, use the encryption protocol adaptive selection model to update the dynamic encryption algorithm of the signal to be transmitted and generate the corresponding updated dynamic encryption algorithm.

[0110] The formula for adaptive selection of the encryption protocol model is: ;

[0111] In the formula, for The backup dynamic encryption algorithm selected for this data acquisition; Value function; The parameter is assigned to the dynamic encryption algorithm in the adaptive selection list for the encryption protocol. The encryption protocol adaptively selects all dynamic encryption algorithms from the list; This represents the total number of dynamic encryption algorithms. This is the result of real-time anomaly detection; ;

[0112] In the formula, for The updated dynamic encryption algorithm for each data acquisition; for The backup dynamic encryption algorithm selected for this data acquisition; for The original dynamic encryption algorithm for this data acquisition; The trigger command is adaptively selected for the real-time encryption protocol in the real-time signal transmission and encryption optimization scheme;

[0113] When conditions permit, quantum key distribution technology is used to generate keys, enabling theoretically unbreakable encrypted communication. Based on real-time security threat levels and computing resources, encryption algorithms (such as Advanced Encryption Standard (AES), Chinese national cryptographic algorithms, etc.) and key lengths are dynamically selected. Based on current network protocols and explosion-proof security requirements, encryption protocols (such as Transport Layer Security (TLS), Internet Protocol Security (IPsec), etc.) are adaptively selected. For resource-constrained devices, lightweight encryption algorithms are used to reduce computational overhead and energy consumption while ensuring security. In this embodiment, the updated dynamic encryption algorithm integrates AES-256 encryption and the Chinese national cryptographic algorithm SM4, meeting the Level 3 requirements of the Information Security Protection Scheme 2.0.

[0114] S7: Based on the real-time dynamic encryption algorithm, the signal to be transmitted by the IoT mobile handheld device is encrypted, and the encrypted signal to be transmitted is transmitted by updating the signal transmission channel.

[0115] Example 2:

[0116] like Figure 2 As shown, this embodiment provides a signal transmission and encryption system for IoT mobile handheld devices in explosion-proof scenarios, which is used to implement a signal transmission and encryption method for IoT mobile handheld devices in explosion-proof scenarios. The system includes a status monitoring data acquisition unit, a health index dynamic calculation unit, a status anomaly detection unit, a signal transmission and encryption optimization unit, a signal transmission link selection unit, an encryption protocol adaptive selection unit, and a signal transmission and encryption execution unit connected in sequence.

[0117] The status monitoring data acquisition unit is used to collect real-time multi-dimensional status monitoring data of the current signal transmission channel of IoT mobile handheld devices in explosion-proof scenarios in a multi-channel signal transmission fusion network.

[0118] The health index dynamic calculation unit is used to obtain the real-time health index of real-time multi-dimensional status monitoring data using the health index dynamic calculation model.

[0119] The status anomaly detection unit is used to perform status anomaly detection on real-time multi-dimensional status monitoring data using a status anomaly detection data model, and obtain real-time status anomaly detection results.

[0120] The signal transmission and encryption optimization unit is used to optimize signal transmission and encryption based on the real-time anomaly detection results using the signal transmission and encryption optimization model, and obtain a real-time signal transmission and encryption optimization scheme.

[0121] The signal transmission link selection unit is used to switch the signal transmission channels of the multi-channel signal transmission fusion network according to the real-time signal transmission and encryption optimization scheme and the signal transmission link selection model, so as to obtain the updated signal transmission channel.

[0122] The encryption protocol adaptive selection unit is used to update the dynamic encryption algorithm of the signal to be transmitted based on the real-time signal transmission and encryption optimization scheme, using the encryption protocol adaptive selection model, and generate the corresponding updated dynamic encryption algorithm.

[0123] The signal transmission and encryption execution unit is used to encrypt the signal to be transmitted by the IoT mobile handheld device according to the real-time dynamic encryption algorithm, and to transmit the encrypted signal by updating the signal transmission channel.

[0124] This invention discloses a method and system for signal transmission and encryption of IoT mobile handheld devices in explosion-proof scenarios. By collecting multi-dimensional status monitoring data in real time, including signal quality, network status, device status, and environmental status, the system can comprehensively perceive the health status of the current signal transmission channel. Using a dynamic health index calculation model, the system quantitatively evaluates the real-time multi-dimensional status monitoring data, promptly detecting anomalies in the signal transmission channel. Based on the anomaly detection results, a multi-modal link selection model intelligently switches to the optimal signal transmission channel, effectively avoiding signal interruptions caused by single-channel failures and significantly improving the reliability of signal transmission. For the safety requirements of explosion-proof scenarios, this invention employs an adaptive encryption protocol selection model based on the real-time status... Based on anomaly detection results, the most suitable encryption algorithm is dynamically selected. This real-time dynamic encryption algorithm encrypts the signals to be transmitted from IoT mobile handheld devices, ensuring data security during transmission and effectively resisting dynamically changing security threats. Combined with an updated signal transmission channel, encrypted signal transmission further enhances data transmission security, meeting the high security standards required in explosion-proof scenarios. Through real-time monitoring and dynamic adjustment mechanisms, it can quickly respond to changes in signal transmission channels and network status, reducing data transmission latency. The introduction of a multimodal link selection model and an adaptive encryption protocol selection model optimizes the signal transmission path and encryption process, improving data transmission efficiency and meeting the high real-time requirements of explosion-proof scenarios.

[0125] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for signal transmission and encryption of IoT mobile handheld devices in explosion-proof scenarios, characterized in that: Includes the following steps: Collect real-time multi-dimensional status monitoring data of the current signal transmission channel of IoT mobile handheld devices in explosion-proof scenarios in a multi-channel signal transmission fusion network; The real-time multi-dimensional status monitoring data includes the real-time signal quality status of the signal to be transmitted, the real-time transmission network status of the current signal transmission channel, the real-time device status of the IoT mobile handheld device, and the real-time environmental status of the explosion-proof scenario. The real-time signal quality status includes real-time signal loss rate, real-time noise level, real-time delay jitter, and real-time signal strength; The real-time transmission network status includes real-time link bandwidth utilization, real-time packet loss rate, real-time round-trip time, real-time QoS level, and real-time backup network type and signal strength. The real-time device status includes real-time battery level, real-time device temperature, real-time device vibration, and real-time device location. The real-time environmental conditions include real-time temperature, real-time humidity, real-time air pressure, real-time combustible gas concentration, and real-time electromagnetic field strength. The real-time health index of the real-time multidimensional status monitoring data is obtained using the health index dynamic calculation model. If the real-time health index exceeds the health index threshold, proceed to the next step. Using a state anomaly detection data model, state anomalies are detected in real-time multidimensional state monitoring data to obtain real-time state anomaly detection results. Based on the real-time anomaly detection results, a signal transmission and encryption optimization model is used to optimize signal transmission and encryption, resulting in a real-time signal transmission and encryption optimization scheme. Based on the real-time signal transmission and encryption optimization scheme, the signal transmission link selection model is used to switch the signal transmission channels of the multi-channel signal transmission fusion network to obtain the updated signal transmission channels. Based on the real-time signal transmission and encryption optimization scheme, an adaptive selection model of the encryption protocol is used to update the dynamic encryption algorithm of the signal to be transmitted and generate the corresponding updated dynamic encryption algorithm. The signal to be transmitted from the IoT mobile handheld device is encrypted using a real-time dynamic encryption algorithm, and the encrypted signal is transmitted by updating the signal transmission channel.

2. The method for signal transmission and encryption of IoT mobile handheld devices in explosion-proof scenarios according to claim 1, characterized in that: The state anomaly detection data model is constructed based on the LSTM-attention-MLP algorithm, and includes a data feature extraction module constructed based on the LSTM algorithm, an attention weight module constructed based on the attention mechanism, and a state anomaly detection module constructed based on the MLP algorithm, which are connected in sequence.

3. The method for signal transmission and encryption of IoT mobile handheld devices in explosion-proof scenarios according to claim 2, characterized in that: Using a state anomaly detection data model, state anomalies are detected in real-time multidimensional state monitoring data to obtain real-time state anomaly detection results. The process includes the following steps: The real-time multidimensional status monitoring data is preprocessed to obtain preprocessed real-time multidimensional status monitoring data, which is then input into the status anomaly detection data model. The data feature extraction module of the state anomaly detection data model is used to extract real-time data features of the preprocessed real-time multidimensional state monitoring data. Based on the preset attention weight module, the attention weight module of the state anomaly detection data model is used to perform weighted fusion of several real-time data feature components to obtain real-time fused features. Based on the real-time fusion characteristics, the state anomaly detection module of the state anomaly detection data model is used to perform state anomaly detection and obtain real-time state anomaly detection results.

4. The method for signal transmission and encryption of IoT mobile handheld devices in explosion-proof scenarios according to claim 3, characterized in that: The signal transmission and encryption optimization model is constructed based on the MOCPO algorithm, and includes an influence factor generation module, an optimization target update module, an initialization module, an iterative optimization module, and a vector decoding module connected in sequence.

5. A signal transmission and encryption system for IoT mobile handheld devices in explosion-proof scenarios, used to implement the signal transmission and encryption method for IoT mobile handheld devices in explosion-proof scenarios as described in any one of claims 1-4, characterized in that: The system includes a status monitoring data acquisition unit, a health index dynamic calculation unit, a status anomaly detection unit, a signal transmission and encryption optimization unit, a signal transmission link selection unit, an encryption protocol adaptive selection unit, and a signal transmission and encryption execution unit, which are connected in sequence.

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