Signal transmission and encryption method and system for mobile handheld equipment of Internet of Things in explosion-proof scene

Abstract

The invention belongs to the technical field of signal transmission, and discloses a signal transmission and encryption method and system for an anti-explosion scene Internet of Things mobile handheld device. The method comprises the following steps: collecting real-time multi-dimensional state monitoring data of the Internet of Things mobile handheld device in an explosion-proof scene; acquiring a real-time health index of the real-time multi-dimensional state monitoring data, and if the real-time health index exceeds a health index threshold, entering the next step; state abnormity detection is carried out; signal transmission and encryption optimization are carried out; switching a signal transmission channel of the multi-path signal transmission fusion network; updating the dynamic encryption algorithm of the signal to be transmitted; and according to the real-time dynamic encryption algorithm, encrypting a to-be-transmitted signal of the Internet of Things mobile handheld device, and performing signal transmission on the encrypted to-be-transmitted signal by updating the signal transmission channel. According to the invention, the problems of unstable signal quality, low transmission security and insufficient real-time performance in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the field of signal transmission technology, and specifically relates to a signal transmission and encryption method and system for a mobile handheld device of the Internet of Things in explosion-proof scenarios. Background Art

[0002] In explosion-proof environments like petrochemicals, coal mines, and gas stations, the presence of hazardous substances like flammable and explosive gases and dust can easily lead to safety accidents. Therefore, IoT mobile handheld devices are required for real-time signal transmission. Ensuring the stability, reliability, and security of signal transmission from mobile handheld devices has become a key research topic in this field.

[0003] The existing technology has the following defects: 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, signal quality is prone to fluctuations due to environmental factors (such as electromagnetic interference and signal attenuation), resulting in reduced data transmission reliability. 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; 3) Insufficient real-time performance: Traditional signal transmission and encryption methods have limitations in processing speed and are unable to meet the high real-time requirements in explosion-proof scenarios. This is especially true when dealing with large amounts of data or poor network conditions, which can easily lead to data transmission delays and affect system response speed. Summary of the Invention

[0004] In order to solve the problems of unstable signal quality, low transmission security and insufficient real-time performance in the prior art, the present invention aims to provide a signal transmission and encryption method and system for mobile handheld devices of the Internet of Things in explosion-proof scenarios.

[0005] The technical solution adopted in the present invention is: A method for signal transmission and encryption of mobile handheld devices in explosion-proof IoT scenarios, comprising the following steps: Collect real-time multi-dimensional status monitoring data of the current signal transmission channel of IoT mobile handheld devices in a multi-channel signal transmission fusion network in explosion-proof scenarios;

[0006] Use the health index dynamic calculation model to obtain the real-time health index of the real-time multi-dimensional status monitoring data. If the real-time health index exceeds the health index threshold, proceed to the next step;

[0007] Use the state anomaly detection data model to perform state anomaly detection on real-time multi-dimensional state monitoring data to obtain real-time state anomaly detection results;

[0008] According to the real-time status anomaly detection results, the signal transmission and encryption optimization model is used to optimize the signal transmission and encryption, and a real-time signal transmission and encryption optimization solution is obtained;

[0009] According to the real-time signal transmission and encryption optimization scheme, the signal transmission link selection model is used to switch the signal transmission channel of the multi-channel signal transmission fusion network to obtain an updated signal transmission channel;

[0010] According to the real-time signal transmission and encryption optimization scheme, the encryption protocol adaptive selection model is used to update the dynamic encryption algorithm of the transmitted signal and generate the corresponding updated dynamic encryption algorithm;

[0011] According to the real-time dynamic encryption algorithm, the signal to be transmitted of the mobile handheld device of the Internet of Things is encrypted, and the encrypted signal to be transmitted is transmitted by updating the signal transmission channel.

[0012] Furthermore, 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 scene;

[0013] Real-time signal quality status includes real-time signal loss rate, real-time noise level, real-time delay jitter and real-time signal strength;

[0014] Real-time transmission network status includes real-time link bandwidth utilization, real-time packet loss rate, real-time round-trip delay, real-time QoS level, and real-time backup network type and signal strength;

[0015] Real-time device status includes real-time battery level, real-time device temperature, real-time device vibration, and real-time device location;

[0016] Real-time environmental status includes real-time temperature, real-time humidity, real-time air pressure, real-time combustible gas concentration, and real-time electromagnetic field strength.

[0017] Furthermore, the formula for the dynamic calculation model of the health index is: ;

[0018] Where, It is a real-time health index; is the environmental correction factor; for The threshold value of the data collection; for Multi-dimensional condition monitoring data collected by secondary data; It is the minimum value of historical multi-dimensional status monitoring data; for The health index weight of the data collection; N is the total number of dimensions of the multidimensional status monitoring data; It is an indicator of the number of data collection times; It is the dimension indicator of multi-dimensional status monitoring data; for Quality factor of the secondary data collection; ;

[0019] Where, , for The threshold value of the data collection; is the smoothing coefficient; for Multi-dimensional condition monitoring data collected by secondary data; ;

[0020] Where, for Health index weight of the data collection; is the compensation coefficient; The maximum value of historical multi-dimensional status monitoring data and the maximum value of health index weight; for Multi-dimensional condition monitoring data collected by the secondary data.

[0021] 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.

[0022] Furthermore, using the state anomaly detection data model, state anomaly detection is performed on the real-time multi-dimensional state monitoring data to obtain real-time state anomaly detection results, including the following steps:

[0023] Preprocessing the real-time multi-dimensional state monitoring data to obtain preprocessed real-time multi-dimensional state monitoring data, and inputting the preprocessed data into a state anomaly detection data model;

[0024] Use the data feature extraction module of the state anomaly detection data model to extract the real-time data features of the pre-processed real-time multi-dimensional state monitoring data;

[0025] According to the preset attention weight module, the attention weight module of the state anomaly detection data model is used to perform weighted fusion on several real-time data feature components of the real-time data feature to obtain a real-time fusion feature;

[0026] According to the real-time fusion features, 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.

[0027] Furthermore, the formula for state anomaly detection is: ;

[0028] Where, Real-time status anomaly detection results; Real-time multi-dimensional status monitoring data extracted by the data feature extraction module Middle Dimension real-time data features; It is a preset attention weight module; is the state anomaly detection prediction function, and the distribution probability is obtained. The predicted label with the highest distribution probability obtained by performing state anomaly detection based on real-time fusion features is used as the real-time state anomaly detection result; is the health index threshold.

[0029] 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 which are connected in sequence.

[0030] Furthermore, the formula of the signal transmission link selection model is: ;

[0031] Where, for A backup signal transmission channel selected for secondary data collection; is the utility function; It refers to the parameters of the signal transmission channel in the multi-channel signal transmission fusion network; All signal transmission channels in the multi-channel signal transmission convergence network; is the total number of signal transmission channels; Real-time status anomaly detection results; ;

[0032] Where, for Update signal transmission channel for secondary data collection; for A backup signal transmission channel selected for secondary data collection; for The original signal transmission channel for data acquisition; A trigger instruction is selected for a real-time signal transmission link in a real-time signal transmission and encryption optimization solution.

[0033] Furthermore, the formula of the encryption protocol adaptive selection model is: ;

[0034] Where, for Backup dynamic encryption algorithm selected for secondary data collection; is the value function; Dynamic encryption algorithm reference parameters in the adaptive selection list for encryption protocols; Adaptively select all dynamic encryption algorithms in the list for the encryption protocol; is the total number of dynamic encryption algorithms; Real-time status anomaly detection results; ;

[0035] Where, for Updated dynamic encryption algorithm for secondary data collection; for Backup dynamic encryption algorithm selected for secondary data collection; for The original dynamic encryption algorithm for data collection; Adaptive selection of trigger instructions for real-time encryption protocols in real-time signal transmission and encryption optimization solutions.

[0036] A signal transmission and encryption system for mobile handheld devices of the Internet of Things in explosion-proof scenarios is used to implement a signal transmission and encryption method for mobile handheld devices of the Internet of Things 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, which are connected in sequence.

[0037] The beneficial effects of the present invention are as follows: the present invention discloses a signal transmission and encryption method and system for mobile handheld devices of the Internet of Things in explosion-proof scenarios. By real-time collection of multi-dimensional status monitoring data, including signal quality, network status, device status and environmental status, the present invention can fully perceive the health status of the current signal transmission channel, and utilize a dynamic calculation model of a health index to quantitatively evaluate the real-time multi-dimensional status monitoring data, and timely discover abnormal conditions of the signal transmission channel. Based on the abnormality detection results of the status, the present invention intelligently switches to the optimal signal transmission channel through a multi-modal link selection model, effectively avoiding signal interruption caused by a single channel failure and significantly improving the reliability of signal transmission. In response to the safety requirements of explosion-proof scenarios, the present invention adopts an encryption protocol adaptive selection model based on the According to the real-time status anomaly detection results, the most appropriate encryption algorithm is dynamically selected. The real-time dynamic encryption algorithm is used to encrypt the signals to be transmitted by the IoT mobile handheld device, ensuring the security of the data during transmission and effectively resisting dynamically changing security threats. The updated signal transmission channel is combined with encrypted signal transmission, which further improves the security of data transmission and meets the high safety standards 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 delays. The introduction of multimodal link selection models and encryption protocol adaptive selection models optimizes the signal transmission path and encryption process, improves the efficiency of data transmission, and meets the high real-time requirements in explosion-proof scenarios.

[0038] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flowchart of the signal transmission and encryption method for mobile handheld devices in explosion-proof scenarios of the Internet of Things in the present invention.

[0040] Figure 2 This is a structural block diagram of the signal transmission and encryption system for mobile handheld devices in explosion-proof scenarios of the Internet of Things in the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments.

[0042] Example 1: like Figure 1 As shown, this embodiment provides a method for signal transmission and encryption of a mobile handheld device of the Internet of Things in an explosion-proof scenario, including the following steps:

[0043] S1: Collects real-time multi-dimensional status monitoring data of the current signal transmission channel of IoT mobile handheld devices in a multi-channel signal transmission fusion network in explosion-proof scenarios;

[0044] 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 IoT mobile handheld devices, and the real-time environmental status of the explosion-proof scene;

[0045] Multimodal communication fusion network, including but not limited to: Satellite-ground dual link: Beidou satellite communication (supporting low-orbit satellite enhancement) is used as the primary link, and the ground network (4G / 5G, explosion-proof WiFi 6) is used as the auxiliary link to achieve seamless switching in complex environments. The satellite link can penetrate buildings and inclement weather, and the ground network provides high bandwidth.

[0046] Real-time signal quality status includes real-time signal loss rate, real-time noise level, real-time delay jitter and real-time signal strength;

[0047] Real-time transmission network status includes real-time link bandwidth utilization, real-time packet loss rate, real-time round-trip delay, real-time Quality of Service (QoS) level, and real-time backup network type (such as Wi-Fi, cellular network, Bluetooth, etc.) and signal strength;

[0048] Real-time device status includes obtaining real-time battery power, real-time device temperature, real-time device vibration, and real-time device location of IoT mobile handheld devices through built-in sensors or external positioning systems;

[0049] Real-time environmental status includes 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;

[0050] S2: Use the health index dynamic calculation model to obtain the real-time health index of the real-time multi-dimensional status monitoring data. If the real-time health index exceeds the health index threshold, proceed to the next step;

[0051] The formula for the dynamic calculation model of health index is: ;

[0052] Where, It is a real-time health index; is the environmental correction factor; for The threshold value of the data collection; for Multi-dimensional condition monitoring data collected by secondary data; It is the minimum value of historical multi-dimensional status monitoring data; for The health index weight of the data collection; N is the total number of dimensions of the multidimensional status monitoring data; It is an indicator of the number of data collection times; It is the dimension indicator of multi-dimensional status monitoring data; for Quality factor of the secondary data collection; ;

[0053] Where, for The threshold value of the data collection; is the smoothing coefficient; for Multi-dimensional condition monitoring data collected by secondary data; ;

[0054] Where, for Health index weight of the data collection; is the compensation coefficient; The maximum value of historical multi-dimensional status monitoring data and the maximum value of health index weight; for Multi-dimensional condition monitoring data collected by secondary data;

[0055] S3: Use the state anomaly detection data model to perform state anomaly detection on the real-time multi-dimensional state monitoring data to obtain real-time state anomaly detection results;

[0056] The state anomaly detection data model is built based on the Long Short Term Memory (LSTM)-attention-Multilayer Perceptron (MLP) algorithm. The state anomaly detection data model includes a data feature extraction module based on the LSTM algorithm, an attention weight module based on the attention mechanism, and a state anomaly detection module based on the MLP algorithm.

[0057] Using the state anomaly detection data model, state anomaly detection is performed on real-time multi-dimensional state monitoring data to obtain real-time state anomaly detection results, including the following steps:

[0058] S3-1: Preprocess the real-time multi-dimensional state monitoring data to obtain preprocessed real-time multi-dimensional state monitoring data, and input the preprocessed data into the state anomaly detection data model;

[0059] S3-2: Use the data feature extraction module of the state anomaly detection data model to extract real-time data features of the pre-processed real-time multi-dimensional state monitoring data;

[0060] S3-3: According to the preset attention weight module, the attention weight module of the state anomaly detection data model is used to perform weighted fusion on several real-time data feature components of the real-time data feature to obtain a real-time fused feature;

[0061] S3-4: Based on the real-time fusion features, 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;

[0062] The formula for state anomaly detection is: ;

[0063] Where, Real-time status anomaly detection results; Real-time multi-dimensional status monitoring data extracted by the data feature extraction module Middle Dimension real-time data features; It is a preset attention weight module; is the state anomaly detection prediction function, and the distribution probability is obtained. The predicted label with the highest distribution probability obtained by performing state anomaly detection based on real-time fusion features is used as the real-time state anomaly detection result; is the health index threshold;

[0064] S4: Based on the real-time status anomaly detection results, the signal transmission and encryption optimization model is used to optimize the signal transmission and encryption to obtain a real-time signal transmission and encryption optimization solution;

[0065] The signal transmission and encryption optimization model is built based on the Multi-objective Crested Porcupine Optimizer (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.

[0066] Based on the real-time status anomaly detection results, the signal transmission and encryption optimization model is used to optimize signal transmission and encryption, and a real-time signal transmission and encryption optimization solution is obtained, which includes the following steps:

[0067] S4-1: Based on the real-time status anomaly detection results, use the impact factor generation module of the signal transmission and encryption optimization model to generate the corresponding real-time impact factor;

[0068] Simultaneously considering multiple influencing factors, such as signal transmission stability, transmission efficiency, integrity, encryption complexity, and encryption security, through an iterative optimization process, it is possible to find a balance between multiple objectives and generate signal transmission and encryption optimization solutions that meet various needs, adapting to the diverse needs of different transmission channels and signal conditions to be transmitted. In this embodiment, the real-time influencing factors include signal transmission cost, signal transmission loss rate, and encryption complexity.

[0069] S4-2: Based on the real-time influencing factor, the optimization target update module of the signal transmission and encryption optimization model is used to update the predicted optimization target to obtain the real-time optimization target, and the real-time fitness function is set according to the real-time optimization target;

[0070] The formula is: ;

[0071] Where, is the real-time fitness function; is the signal transmission cost function; is the signal transmission loss rate function; is the encryption complexity function; For MOCPO individuals; are the first weight value, the second weight value, and the third weight value;

[0072] S4-3: Encode the initial real-time signal transmission and encryption optimization scheme into individual vectors 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, thereby obtaining an initial MOCPO population consisting of several initial MOCPO individuals.

[0073] The formula is: ;

[0074] Where, is the initial MOCPO individual of the Circle chaos map, i.e. the initial solution; The initial MOCPO individuals are randomly generated; It is the MOCPO individual indicator; The initial population generated by the Circle Chaotic Map sequence is compared with the randomly distributed population. The initial position distribution of the improved MOCPO individuals is more uniform, which expands the search range of the algorithm in space and increases the diversity of group positions. To a certain extent, it improves the defect that the algorithm is prone to falling into local extreme values, thereby improving the optimization efficiency of the algorithm.

[0075] S4-4: Based on the real-time fitness function, use the iterative optimization module of the signal transmission and encryption optimization model to iteratively optimize several initial solutions to obtain the optimal solution, including the following steps:

[0076] S4-4-1: Set 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;

[0077] The formula is: ;

[0078] Where, For the The number of individuals in the MOCPO population parameter of the iteration; For the The number of individuals in the MOCPO population parameter of the iteration; is the minimum number of individuals in the MOCPO population parameter; Evaluate arguments for functions; Evaluate loop parameters for a function; is the maximum function evaluation loop parameter; t is the number of iterations indicator;

[0079] S4-4-2: Calculate the initial fitness value of the initial MOCPO individual in the initial MOCPO population according to the real-time fitness function;

[0080] S4-4-3: Based on the initial fitness value and the updated MOCPO population parameters, the first defense strategy, the second defense strategy, the third defense strategy, and the fourth defense strategy are used to update the initial MOCPO population to obtain an updated MOCPO population;

[0081] The formula for the first defense strategy is: ;

[0082] Where, For the updated MOCPO individuals within the first defense range; The initial MOCPO individual within the first defense range; is a random number based on normal distribution; is a random value in the interval [0,1]; It is the optimal solution within the first defense range; is the vector generated between the true optimal solution within the first defense range and the optimal solution randomly selected from the MOCPO population; It is the MOCPO individual indicator; is the iteration indicator;

[0083] The formula for the second defense strategy is: ;

[0084] Where, For the updated MOCPO individuals within the second defense range; The initial MOCPO individual within the second defense range; is the search upper limit vector of the second defense range; is a random value in the interval [0,1]; Respectively Initial MOCPO individuals; Both Two random integers between; is the vector generated between the true optimal solution within the second defense range and the optimal solution randomly selected from the MOCPO population;

[0085] The formula for the third defense strategy is: ;

[0086] Where, For the updated MOCPO individuals within the third defense range; The initial MOCPO individual within the third defense range; is the search upper limit vector of the third defense range; Respectively Initial MOCPO individuals; for A random integer between ; The odor diffusion factor defined for the fitness function; It is a defense factor; Control parameters for search direction;

[0087] The formula for the fourth defense strategy is: ;

[0088] Where, For the updated MOCPO individuals within the fourth defense range; For the initial MOCPO individual within the fourth defense range; It is the optimal solution within the fourth defense range; All are random values in the interval [0,1]; It is a defense factor; Control parameters for search direction; is the average force affecting the search direction; is the convergence speed factor;

[0089] S4-4-4: Use the dynamic reverse learning algorithm to perform dynamic reverse learning on the updated MOCPO population to generate a dynamic reverse MOCPO population;

[0090] The formula is: ;

[0091] Where, It is a dynamically reversed MOCPO individual; is the decreasing inertia coefficient; are the maximum and minimum values of the vector space respectively; For the newer MOCPO individuals;

[0092] S4-4-5: Calculate the fitness values of all MOCPO individuals in the updated MOCPO population and the dynamically reversed MOCPO population according to the real-time fitness function, take the MOCPO individual with the minimum fitness value as the optimal individual, and retain the optimal individual;

[0093] S4-4-6: If the number of iterations of iterative optimization reaches the maximum number of iterations or the fitness value of the optimal individual meets the requirements, the optimal solution corresponding to the optimal individual is output;

[0094] S4-5: Use the vector decoding module of the signal transmission and encryption optimization model to decode the individual vectors of the optimal solution to obtain the optimal real-time signal transmission and encryption optimization solution;

[0095] S5: Based on the real-time signal transmission and encryption optimization solution, the signal transmission link selection model is used to switch the signal transmission channel of the multi-channel signal transmission fusion network to obtain an updated signal transmission channel, such as switching from Wi-Fi to the signal transmission channel of the cellular network, or selecting a signal transmission channel that is more suitable for explosion-proof scenarios;

[0096] The formula for the signal transmission link selection model is: ;

[0097] Where, for A backup signal transmission channel selected for secondary data collection; is the utility function; It refers to the parameters of the signal transmission channel in the multi-channel signal transmission fusion network; All signal transmission channels in the multi-channel signal transmission convergence network; is the total number of signal transmission channels; Real-time status anomaly detection results; ;

[0098] Where, for Update signal transmission channel for secondary data collection; for A backup signal transmission channel selected for secondary data collection; for The original signal transmission channel for data acquisition; Selecting trigger instructions for a real-time signal transmission link in a real-time signal transmission and encryption optimization solution;

[0099] S6: Based on the real-time signal transmission and encryption optimization scheme, the encryption protocol adaptive selection model is used to update the dynamic encryption algorithm of the transmitted signal and generate a corresponding updated dynamic encryption algorithm;

[0100] The formula of the encryption protocol adaptive selection model is: ;

[0101] Where, for Backup dynamic encryption algorithm selected for secondary data collection; is the value function; Dynamic encryption algorithm reference parameters in the adaptive selection list for encryption protocols; Adaptively select all dynamic encryption algorithms in the list for the encryption protocol; is the total number of dynamic encryption algorithms; Real-time status anomaly detection results; ;

[0102] Where, for Updated dynamic encryption algorithm for secondary data collection; for Backup dynamic encryption algorithm selected for secondary data collection; for The original dynamic encryption algorithm for data collection; Adaptively select trigger instructions for real-time encryption protocols in real-time signal transmission and encryption optimization solutions;

[0103] When conditions permit, quantum key distribution technology is used to generate keys to achieve theoretically unbreakable encrypted communications. Based on the real-time security threat level and computing resources, encryption algorithms (such as Advanced Encryption Standard (AES), national secret algorithms, etc.) and key lengths are dynamically selected. Based on current network protocols and explosion-proof safety 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 computing overhead and energy consumption while ensuring security. In this embodiment, the updated dynamic encryption algorithm integrates AES-256 encryption and the national secret SM4 algorithm to meet the Level 3 requirements of Information Security Protection 2.0.

[0104] S7: Encrypt the signal to be transmitted of the mobile handheld device of the Internet of Things according to the real-time dynamic encryption algorithm, and transmit the encrypted signal to be transmitted by updating the signal transmission channel.

[0105] Example 2: like Figure 2 As shown, this embodiment provides a signal transmission and encryption system for mobile handheld devices in explosion-proof IoT scenarios, which is used to implement a signal transmission and encryption method for mobile handheld devices in explosion-proof IoT 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, which are connected in sequence.

[0106] The status monitoring data acquisition unit is used to collect real-time multi-dimensional status monitoring data of the current signal transmission channel of the IoT mobile handheld device in the multi-channel signal transmission fusion network in the explosion-proof scenario;

[0107] A health index dynamic calculation unit, configured to obtain a real-time health index of real-time multi-dimensional status monitoring data using a health index dynamic calculation model;

[0108] A state anomaly detection unit is used to perform state anomaly detection on real-time multi-dimensional state monitoring data using a state anomaly detection data model to obtain real-time state anomaly detection results;

[0109] A signal transmission and encryption optimization unit is used to optimize signal transmission and encryption based on the real-time state anomaly detection result using a signal transmission and encryption optimization model to obtain a real-time signal transmission and encryption optimization solution;

[0110] A signal transmission link selection unit is configured to switch the signal transmission channel of the multi-channel signal transmission fusion network using a signal transmission link selection model according to a real-time signal transmission and encryption optimization solution to obtain an updated signal transmission channel;

[0111] An encryption protocol adaptive selection unit is used to update the dynamic encryption algorithm of the signal to be transmitted using an encryption protocol adaptive selection model according to the real-time signal transmission and encryption optimization scheme, and generate a corresponding updated dynamic encryption algorithm;

[0112] The signal transmission and encryption execution unit is used to encrypt the signal to be transmitted of the mobile handheld device of the Internet of Things according to the real-time dynamic encryption algorithm, and transmit the encrypted signal to be transmitted by updating the signal transmission channel.

[0113] The present invention discloses a method and system for signal transmission and encryption of mobile handheld devices of the Internet of Things in explosion-proof scenarios. By real-time collection of multi-dimensional status monitoring data, including signal quality, network status, device status and environmental status, the method and system can fully perceive the health status of the current signal transmission channel. The method uses a dynamic calculation model of the health index to quantitatively evaluate the real-time multi-dimensional status monitoring data, and promptly discover abnormal conditions of the signal transmission channel. Based on the abnormality detection results, the method intelligently switches to the optimal signal transmission channel through a multi-modal link selection model, effectively avoiding signal interruption caused by a single channel failure and significantly improving the reliability of signal transmission. In response to the safety requirements of explosion-proof scenarios, the present invention adopts an encryption protocol adaptive selection model, which selects the optimal signal transmission channel according to the real-time status. Based on the abnormal detection results, the most appropriate encryption algorithm is dynamically selected. Through the real-time dynamic encryption algorithm, the signals to be transmitted by the IoT mobile handheld devices are encrypted to ensure the security of data during transmission and effectively resist dynamically changing security threats. The updated signal transmission channel is combined with encrypted signal transmission to further improve the security of data transmission and meet the high safety standards 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 delays. The introduction of multimodal link selection models and encryption protocol adaptive selection models optimizes the signal transmission path and encryption process, improves the efficiency of data transmission, and meets the high real-time requirements in explosion-proof scenarios.

[0114] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method 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 in the scope of protection of the present invention.

Claims

1. A method for signal transmission and encryption of mobile handheld devices in explosion-proof IoT scenarios, characterized by: The steps include: Collect real-time multi-dimensional status monitoring data of the current signal transmission channel of IoT mobile handheld devices in a multi-channel signal transmission fusion network in explosion-proof scenarios; Use the health index dynamic calculation model to obtain the real-time health index of the real-time multi-dimensional status monitoring data. If the real-time health index exceeds the health index threshold, proceed to the next step; Use the state anomaly detection data model to perform state anomaly detection on real-time multi-dimensional state monitoring data to obtain real-time state anomaly detection results; According to the real-time status anomaly detection results, the signal transmission and encryption optimization model is used to optimize the signal transmission and encryption, and a real-time signal transmission and encryption optimization solution is obtained; According to the real-time signal transmission and encryption optimization scheme, the signal transmission link selection model is used to switch the signal transmission channel of the multi-channel signal transmission fusion network to obtain an updated signal transmission channel; According to the real-time signal transmission and encryption optimization scheme, the encryption protocol adaptive selection model is used to update the dynamic encryption algorithm of the transmitted signal and generate the corresponding updated dynamic encryption algorithm; According to the real-time dynamic encryption algorithm, the signal to be transmitted of the mobile handheld device of the Internet of Things is encrypted, and the encrypted signal to be transmitted is transmitted by updating the signal transmission channel.

2. The method for signal transmission and encryption of mobile handheld devices in explosion-proof IoT scenarios according to claim 1, characterized in that: 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 scene; 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 delay, real-time QoS level, and real-time backup network type and signal strength; The real-time device status includes real-time battery power, real-time device temperature, real-time device vibration, and real-time device location; The real-time environmental status includes real-time temperature, real-time humidity, real-time air pressure, real-time combustible gas concentration and real-time electromagnetic field strength.

3. The method for signal transmission and encryption of a mobile handheld device for explosion-proof IoT scenarios according to claim 2, characterized in that: The formula of the health index dynamic calculation model is: ； Where, It is a real-time health index; is the environmental correction factor; for The threshold value of the data collection; for Multi-dimensional condition monitoring data collected by secondary data; It is the minimum value of historical multi-dimensional status monitoring data; for Health index weight of the data collection; N is the total number of dimensions of the multidimensional condition monitoring data; It is an indicator of the number of data collection times; It is the dimension indicator of multi-dimensional status monitoring data; for Quality factor of the data acquisition; ； Where, for The threshold value of the data collection; is the smoothing coefficient; for Multi-dimensional condition monitoring data collected by secondary data; ； Where, for Health index weight of the data collection; is the compensation coefficient; The maximum value of historical multi-dimensional status monitoring data and the maximum value of health index weight; for Multi-dimensional condition monitoring data collected by the secondary data.

4. The method for signal transmission and encryption of a mobile handheld device for explosion-proof IoT scenarios according to claim 3 is characterized by: 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.

5. The method for signal transmission and encryption of a mobile handheld device for explosion-proof IoT scenarios according to claim 4 is characterized in that: Using the state anomaly detection data model, state anomaly detection is performed on real-time multi-dimensional state monitoring data to obtain real-time state anomaly detection results, including the following steps: Preprocessing the real-time multi-dimensional state monitoring data to obtain preprocessed real-time multi-dimensional state monitoring data, and inputting the preprocessed data into a state anomaly detection data model; Use the data feature extraction module of the state anomaly detection data model to extract the real-time data features of the pre-processed real-time multi-dimensional state monitoring data; According to the preset attention weight module, the attention weight module of the state anomaly detection data model is used to perform weighted fusion on several real-time data feature components of the real-time data feature to obtain a real-time fusion feature; According to the real-time fusion features, 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.

6. The method for signal transmission and encryption of a mobile handheld device for explosion-proof IoT scenarios according to claim 5, characterized in that: The formula for state anomaly detection is: ； Where, Real-time status anomaly detection results; Real-time multi-dimensional status monitoring data extracted by the data feature extraction module Middle Dimension real-time data features; It is a preset attention weight module; is the state anomaly detection prediction function, and the distribution probability is obtained. The predicted label with the highest distribution probability obtained by performing state anomaly detection based on real-time fusion features is used as the real-time state anomaly detection result; is the health index threshold.

7. The method for signal transmission and encryption of a mobile handheld device in an explosion-proof IoT scenario according to claim 6, characterized in that: 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 which are connected in sequence.

8. The method for signal transmission and encryption of a mobile handheld device in an explosion-proof IoT scenario according to claim 7, characterized in that: The formula of the signal transmission link selection model is: ； Where, for A backup signal transmission channel selected for secondary data collection; is the utility function; It refers to the parameters of the signal transmission channel in the multi-channel signal transmission fusion network; All signal transmission channels in the multi-channel signal transmission convergence network; is the total number of signal transmission channels; Real-time status anomaly detection results; ； Where, for Update signal transmission channel for secondary data collection; for A backup signal transmission channel selected for secondary data collection; for The original signal transmission channel for data acquisition; A trigger instruction is selected for a real-time signal transmission link in a real-time signal transmission and encryption optimization solution.

9. The method for signal transmission and encryption of a mobile handheld device for explosion-proof IoT scenarios according to claim 8, characterized in that: The formula of the encryption protocol adaptive selection model is: ； Where, for Backup dynamic encryption algorithm selected for secondary data collection; is the value function; Dynamic encryption algorithm reference parameters in the adaptive selection list for encryption protocols; Adaptively select all dynamic encryption algorithms in the list for the encryption protocol; is the total number of dynamic encryption algorithms; Real-time status anomaly detection results; ； Where, for Updated dynamic encryption algorithm for secondary data collection; for Backup dynamic encryption algorithm selected for secondary data collection; for The original dynamic encryption algorithm for data collection; Adaptive selection of trigger instructions for real-time encryption protocols in real-time signal transmission and encryption optimization solutions.

10. A signal transmission and encryption system for an explosion-proof IoT mobile handheld device, for implementing the signal transmission and encryption method for an explosion-proof IoT mobile handheld device according to any one of claims 1 to 9, 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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