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Deep mining in Shaanxi and Inner Mongolia mining areas is characterized by high mine pressure and high degree of broken coal. Leaving narrow coal pillars is easy to cause side air leakage along goaf, and the coal left in the adjacent gob will undergo oxidation reaction to form a hidden danger of coal spontaneous combustion. In order to grasp the spontaneous combustion characteristics of coal along the gob-side, taking Yingpanhao Coal Mine as an example, the characteristics of spontaneous combustion parameters and the variation of each gas in the process of coal oxidation were obtained through temperature-programmed primary oxidation and secondary oxidation experiments. The results show that the critical temperature range of raw coal and oxidized coal in 2201 working face of Yingpanhao Coal Mine is 70-80 ℃ and 65-75 ℃, and the cracking temperature range is 105-115 ℃ and 120-130 ℃. The critical temperature of oxidized coal was earlier than that of raw coal, and the cracking temperature lagged behind. By comparing the oxygen consumption rate and CO production rate during the two oxidation processes, the oxidation degree of oxidized coal was more severe than that of raw coal before 90-100 ℃, and the opposite was true after 90-100 ℃. It shows that coal spontaneous combustion of oxidized coal is more likely to occur in the low temperature stage, and the hidden danger of spontaneous combustion is more obviously. Therefore, by strengthening the monitoring of the spontaneous combustion of coal along the gob-side, the precise control and rapid emergency control methods for the spontaneous combustion of coal along the gob-side in Shaanxi and Inner Mongolia mining areas are proposed, and a combination of air leakage control, colloid pressure injection and liquid cooling CO2 is constructed, which has achieved advanced prevention and control of coal spontaneous combustion disasters along the gob-side, and effectively ensured that there is no fire along the gob-side and the safety of the local gob.

To explore the hazard zone of coal spontaneous combustion in the adiacent goaf at different stages of the roadway driving face, the 2202 working face and the 2201 adjacent goaf of Yingpanhao Coal Mine were taken as examples. The large-scale experiment was conducted to analyze the coal spontaneous combustion limit parameters of the 2201 goaf. The conditions for identifying the hazard zone of coal spontaneous combustion in the adjacent goaf were proposed. Monitoring the gas concentration and temperature in 2201 adjcent goaf by the drilling holes through coal pillar, and the numerical simulation was conducted to study the oxygen concentration distribution in 2201 adjcent goaf, and the hazard zone of coal spontaneous combustion in the adjacent goaf was obtained. The results show that during the mining period of the 2202 working face, the coal pillar was broken by the stress concentration, and the oxygen volume fraction of the adjacent goaf was in the range of 10.1% to 13.8%. The oxygen volume fraction of the adjacent goaf before the stop mining period was between 10.3% and 15%. During the mining period, a narrow area on the coal pillar side with width 55 m from the front 45 m and behind 119 m of 2202 working face in the adjacent goaf was the coal spontaneous combustion hazard zone. Before the stop mining period, a narrow area on the coal pillar side with width 42 m from the front 63 m and behind 107 m of 2202 working face in the adjcent goaf was in an oxidizing environment.

At present, the automatic control method of mine local ventilation adopts manual adjustment of ventilator frequency, and relies on manual collection of air duct parameters, which lacks accurate and reliable monitoring methods to reflect the ventilation status and cannot provide a basis for accurate adjustment of air volume. In order to meet the demand for intelligent construction of local ventilation in mines, an intelligent regulation and control system for local ventilation in mines is proposed, and the overall design of the system is introduced in detail in terms of system composition, principle and function. Based on the real-time monitoring data of multiple sensors, a calculation method for local ventilation parameters and a ventilation system power consumption analysis method are proposed. By analyzing the dynamic distribution of air duct resistance, the air duct resistance and power consumption abnormalities are studied and quickly located. The monitoring parameters are used to simulate the air volume in advance to determine the best supply-demand matching control scheme. According to the law of gas emission from the working face, an intelligent air regulation scheme based on gas emission monitoring and ventilator frequency regulation gas diluting is proposed, and five local ventilator frequency conversion regulation rules are formulated to realize the intelligent matching of local ventilation supply and demand. The Bayesian network algorithm is used to diagnose the working status of local ventilators and sensor equipment, and the rough set and genetic algorithm are used to extract the characteristic samples and precursor information of the normal air supply and fault conditions of the local ventilation. The support vector machine is used to establish local ventilation fault decision-making rules, and local ventilation abnormality diagnosis and early warning model is established to realize the diagnosis and early warning of local ventilation status and development trend. Taking the local ventilation of a mine driving working face as an example, the calculation method of the ventilation parameters of the system has been verified, which provides the basic data for the diagnosis and early warning of local ventilation abnormalities.

With the popularity of the concept of big health and the important role of wearable devices in medical health, users pay more attention to the collection and acquisition of physiological data, but wearable devices attached to human users are independent, and the degree of data sharing is low. Improving the data sharing, accuracy, and reliability of wearable device monitoring is a problem that the article needs to study and solve. Specifically, the researcher summarizes the characteristics of the physiological monitoring smart clothing, and the basic physiological data parameters of human body, and analyzes the collection of three basic signals of electrocardiogram, body temperature, and human movement. This article summarizes the requirements and key technologies of body area network transmission of smart clothing, and studies the body area network node design, energy consumption optimization mode, and network architecture of physiological monitoring smart clothing. At the same time, based on the previous research, the multi-interaction process of smart clothing is formed, and then the standard evaluation system of smart clothing body domain network for evaluation is proposed. The results show that the optimized structure of the body area network of smart clothing proposed by the researchers is efficient, convenient, and mobile, and meets the characteristics of safety, reliability, low power consumption, and portability of smart clothing, especially in the field of physiological monitoring. The standard evaluation system of smart clothing body area network provides a practice-oriented theoretical reference for the current research of smart clothing body area network.

In order to improve the anti-attack performance of the watermark to meet the requirements of copyright protection and content forensics. This paper proposes a digital watermarking hybrid algorithm based on color images. The specific process is to adopt the idea of multi-algorithm layered embedding, choose the algorithm based on discrete cosine transform (DCT) algorithm, discrete wavelet transform_singular value decomposition (DWT_SVD) algorithm, and hologram algorithm, these three algorithms with robust complementary functions, then embed the same watermark image into the color image R, G and B layers to complete the watermark embedding. Compared with the single-algorithm embedded watermark, the hybrid algorithm can achieve blind extraction, at the same time, the algorithm has better robustness and can resist more types and higher intensity attacks. In the process of digital image transmission, the integrity of the watermark information of the carried picture can be guaranteed to achieve copyright protection, content forensics and other purposes.

As the input ratio of regional distribution network increases year by year, higher requirements are put forward for high quality and high reliability of power consumption. Under this background, this paper involves 11 evaluation indexes from three dimensions of reliability, economy and environmental protection, and designs the evaluation index system of investment effect of regional distribution network. The multiattribute AHP-TOPSIS-entropy weight combination evaluation method for the investment effect of regional distribution networks is constructed. And select distribution network A, B, C area for example analysis, the results show that the n-1 pass rate, power supply reliability, power consumption growth and emission reduction of clean energy generation are positively correlated with the investment effect of regional distribution network.