CN-121644368-B - Industrial Internet of things data transmission optimization method in complex environment

CN121644368BCN 121644368 BCN121644368 BCN 121644368BCN-121644368-B

Abstract

The invention relates to the technical field of data transmission, and discloses an industrial Internet of things data transmission optimization method in a complex environment, which comprises the steps of collecting data transmission parameters and environment parameters of industrial Internet of things equipment in the complex environment, and generating an original data transmission data set and an environment parameter data set; the method comprises the steps of preprocessing an original data transmission data set and an environment parameter data set, including data cleaning, normalization and feature extraction, to generate preprocessed data transmission feature data and environment features, collecting data transmission parameters and environment parameters of industrial Internet of things equipment, performing data cleaning, normalization and feature extraction on the data transmission parameters and the environment parameters, to generate preprocessed feature data, to ensure accuracy and reliability of data transmission analysis, and to perform time stamp alignment and space association processing on the environment parameters and the transmission parameters, to sense the influence of complex environments, and to ensure accuracy and integrity of data transmission optimization basic data.

Inventors

ZHANG SHENGXUAN
ZHANG LIANG
YANG KUN

Assignees

上海鼎为物联技术有限公司

Dates

Publication Date: 20260508
Application Date: 20260204

Claims (8)

1. The data transmission optimization method for the industrial Internet of things in the complex environment is characterized by comprising the following steps of: S1, acquiring data transmission parameters and environment parameters of industrial Internet of things equipment in a complex environment, and generating an original data transmission data set and an environment parameter data set; S2, preprocessing the original data transmission data set and the environment parameter data set, including data cleaning, normalization and feature extraction, to generate preprocessed data transmission feature data and environment feature data; S3, carrying out quantitative analysis on the influence of the complex environment on data transmission based on the preprocessed data transmission characteristic data and environment characteristic data, and generating environment interference assessment data, wherein the environment interference comprises signal attenuation, multipath interference and electromagnetic noise; the quantitative analysis of the influence of the complex environment on the data transmission in the S3 comprises the following steps: s31, calculating a path loss index and an interference coefficient in a signal propagation model based on the preprocessed environmental characteristic data, and generating a preliminary environmental interference index; s32, combining the data transmission characteristic data, quantifying the influence degree of the environmental parameters on the data transmission quality by using a gray correlation analysis method, and generating a correlation matrix; S33, generating environmental interference evaluation data through weighted fusion according to the incidence matrix, wherein the evaluation data comprises an interference level, an expected error rate and an available bandwidth; s4, selecting an optimal data transmission strategy by adopting a self-adaptive optimization algorithm according to the environmental interference evaluation data, wherein the data transmission strategy comprises route selection, modulation mode adjustment and transmission power control; and S4, selecting an optimal data transmission strategy, which comprises the following steps: s41, establishing a multi-objective optimization function, wherein the multi-objective optimization function aims at maximizing throughput, minimizing delay and packet loss rate, and constraint conditions comprise equipment energy limit and environmental interference threshold; S42, adopting a reinforcement learning algorithm as a self-adaptive optimization algorithm, and initializing a Q-learning model, wherein a state space is environment interference evaluation data, and an action space is a combination of data transmission strategies; s43, through iterative training of a Q-learning model, selecting a data transmission strategy with the maximum long-term rewards as an optimal data transmission strategy, and outputting strategy parameters; S5, carrying out real-time transmission of industrial Internet of things data according to the optimal data transmission strategy, and dynamically monitoring transmission performance indexes including throughput, delay and packet loss rate in the transmission process; S6, based on a dynamic monitoring result, using a feedback control mechanism to adjust parameters of a data transmission strategy in real time so as to cope with environmental changes; s7, carrying out integrity verification and error recovery processing on the data after transmission, and generating an optimized data transmission report; s8, storing transmission process data and analysis results, and being suitable for subsequent machine learning model training and strategy optimization.
2. The method for optimizing data transmission of the industrial Internet of things in the complex environment according to claim 1, wherein the step S1 of collecting data transmission parameters and environment parameters of the industrial Internet of things equipment in the complex environment comprises the following steps: S11, acquiring real-time data transmission parameters of the connection equipment, including the size of a data packet, transmission frequency, protocol type and equipment identifier, through industrial Internet of things gateway equipment, and generating an original data transmission data set; S12, acquiring complex environmental parameters including temperature, humidity, signal intensity, noise level and moving obstacle distribution by using an environmental sensor network, and generating an environmental parameter data set; And S13, performing time stamp alignment and spatial correlation processing on the original data transmission data set and the environment parameter data set.
3. The method for optimizing data transmission of the industrial Internet of things in a complex environment according to claim 2, wherein the preprocessing in S2 comprises the following steps: s21, carrying out data cleaning on the original data transmission data set, removing abnormal values and repeated data, and filling missing values; S22, carrying out numerical normalization on the cleaned data by adopting a minimum-maximum normalization method, so that all parameters are scaled to a [0,1] interval; S23, extracting key features from the normalized data, including a data stream mode, an environment periodic variation feature and an interference correlation feature, and generating preprocessed data transmission feature data and environment feature data.
4. The method for optimizing industrial Internet of things data transmission in a complex environment according to claim 3, wherein the real-time transmission of the industrial Internet of things data in S5 comprises the following steps: s51, configuring a communication module of industrial Internet of things equipment according to the selected optimal data transmission strategy, wherein the communication module comprises a routing path, a modulation coding scheme and transmitting power; S52, starting data stream transmission, and collecting transmission performance indexes in real time by using an embedded monitoring agent, wherein data is sampled once every preset time window; And S53, aggregating the sampled data to generate a transmission performance log, which is suitable for subsequent dynamic adjustment.
5. The method for optimizing data transmission of industrial Internet of things in a complex environment according to claim 4, wherein the step of adjusting parameters of the data transmission strategy in real time in S6 comprises the following steps: S61, designing a proportional-integral-derivative controller as a feedback control mechanism, inputting the deviation between the real-time transmission performance index and the target value, and outputting the deviation as a strategy parameter adjustment quantity; s62, when the deviation exceeds a preset threshold value, triggering the controller to calculate new routing weight and modulation parameters; and S63, dynamically updating the adjusted parameters to the running data transmission strategy to realize closed-loop optimization.
6. The method for optimizing data transmission of the industrial Internet of things in a complex environment according to claim 5, wherein the integrity verification and error recovery processing in S7 comprises the following steps: S71, at a data receiving end, verifying the data integrity by using a cyclic redundancy check and hash algorithm, and requesting retransmission of an error data packet; S72, adopting a forward error correction coding technology to decode and recover the retransmission data packet, and reducing the repeated transmission times; S73, counting the success rate of transmission, average delay and energy efficiency ratio, and generating an optimized data transmission report.
7. The method for optimizing data transmission of the industrial Internet of things in a complex environment according to claim 6, wherein the step of storing transmission process data and analysis results in the step S8 comprises the following steps: s81, storing transmission process data, environmental interference evaluation data and an optimization report in a cloud platform distributed database; S82, training a deep neural network model by using stored data regularly, and improving the prediction accuracy of the self-adaptive optimization algorithm; s83, updating a strategy library for future transmission task call based on model output.
8. The method for optimizing data transmission of industrial Internet of things in a complex environment according to claim 1, wherein the method further comprises a security enhancement step: s91, in the data transmission process, carrying out end-to-end encryption on sensitive data by an integrated lightweight encryption algorithm AES-128; s92, recording a transmission log by using a block chain technology, so that the data cannot be tampered and traced; S93, implementing an identity authentication and access control mechanism to prevent unauthorized equipment from accessing the network.

Description

Industrial Internet of things data transmission optimization method in complex environment Technical Field The invention relates to the technical field of data transmission, in particular to an industrial Internet of things data transmission optimization method in a complex environment. Background The industrial Internet of things is characterized in that various acquisition and control sensors or controllers with sensing and monitoring capabilities, mobile communication, intelligent analysis and other technologies are continuously integrated into various links of an industrial production process, so that the manufacturing efficiency is greatly improved, the product quality is improved, the product cost and the resource consumption are reduced, and finally the traditional industry is improved to an intelligent new stage. At present, in the data transmission process of the industrial Internet of things under a complex environment, due to various dynamic interference factors, when data transmission is optimized in real time, the adopted traditional transmission strategy is based on a fixed rule or a static model, cannot sense and adapt to the influence of changes such as illumination, electromagnetic noise, moving obstacles and the like in the transmission environment on the signal quality in real time, and can lead to the increase of data transmission delay and the increase of packet loss rate, so that the real-time performance and reliability of the transmission are difficult to ensure, and the overall operation efficiency of the industrial Internet of things is influenced. Therefore, an optimization method for industrial internet of things data transmission in a complex environment is provided to solve the problems. Disclosure of Invention Aiming at the defects of the prior art, the invention provides an industrial Internet of things data transmission optimization method in a complex environment, which solves the problems that the overall operation efficiency of the industrial Internet of things is influenced due to the fact that the data transmission delay is increased, the packet loss rate is increased and the real-time property and reliability of transmission are difficult to ensure in the background art. In order to achieve the purpose, the technical scheme is that the method for optimizing the data transmission of the industrial Internet of things in the complex environment comprises the following steps: S1, acquiring data transmission parameters and environment parameters of industrial Internet of things equipment in a complex environment, and generating an original data transmission data set and an environment parameter data set; S2, preprocessing the original data transmission data set and the environment parameter data set, including data cleaning, normalization and feature extraction, to generate preprocessed data transmission feature data and environment feature data; S3, carrying out quantitative analysis on the influence of the complex environment on data transmission based on the preprocessed data transmission characteristic data and environment characteristic data, and generating environment interference assessment data, wherein the environment interference comprises signal attenuation, multipath interference and electromagnetic noise; s4, selecting an optimal data transmission strategy by adopting a self-adaptive optimization algorithm according to the environmental interference evaluation data, wherein the data transmission strategy comprises route selection, modulation mode adjustment and transmission power control; S5, carrying out real-time transmission of industrial Internet of things data according to the optimal data transmission strategy, and dynamically monitoring transmission performance indexes including throughput, delay and packet loss rate in the transmission process; S6, based on a dynamic monitoring result, using a feedback control mechanism to adjust parameters of a data transmission strategy in real time so as to cope with environmental changes; s7, carrying out integrity verification and error recovery processing on the data after transmission, and generating an optimized data transmission report; s8, storing transmission process data and analysis results, and being suitable for subsequent machine learning model training and strategy optimization. Preferably, the step S1 of collecting data transmission parameters and environmental parameters of the industrial internet of things device in a complex environment includes the following steps: S11, acquiring real-time data transmission parameters of the connection equipment, including the size of a data packet, transmission frequency, protocol type and equipment identifier, through industrial Internet of things gateway equipment, and generating an original data transmission data set; S12, acquiring complex environmental parameters including temperature, humidity, signal intensity, noise level and moving obstacle distribution b