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In modern safety management, it is very important to study the influence of the whole safety system on unsafe acts in order to prevent accidents. However, theoretical research in this area is sparse. In order to obtain the influence law of various factors in the safety system on unsafe acts, this paper used system dynamics simulation to carry out theoretical research. First, based on a summary of the causes of the coal and gas outburst accidents, a dynamic simulation model for unsafe acts was established. Second, the system dynamics model is applied to investigate the influence of various safety system factors on unsafe acts. Third, the mechanism and the control measures of unsafe acts in the enterprise safety system are studied. This study’s main result and conclusions are as follows: (1) In the new coalmines, the influence of the safety culture, safety management system, and safety ability on the safety acts were similar. The order of influence on the safety acts in production coalmines is as follows: safety management system > safety ability > safety culture. The difference is most evident in months ten to eighteen. The higher the safety level and safety construction standard of the company, the greater the difference. (2) In the construction of the safety culture, the order of influence was as follows: safety measure elements > safety responsibility elements = safety discipline elements > safety concept elements. It shows the difference in influence from the 6th month and attains its maximum value from the 12th month to the 14th month. (3) In the construction of the safety management system, the degree of influence in new coalmines was as follows: safety policy > safety management organization structure > safety management procedures. Among them, especially in the first 18 months, the impact of the safety policy was most apparent. However, in the production mine, the degree of influence was as follows: safety management organization structure > safety management procedures > safety policy, but the difference is very small. (4) The degree of influence on the construct of safety ability was as follows: safety knowledge > safety psychology = safety habits > safety awareness, but the difference on the impact was small.

The specific risk assessment of informal settlements (IS) is important for the management of IS and protection of environmental safety and public health. In this paper, we introduced the different types of IS in China, and conducted the fire risk assessment on 26 burning buildings in these IS, providing a semi-quantitative and scenario fire risk perception of IS in China for the readers. Two methods, the risk index and the Bayesian network, are proposed and adopted for the fire risk assessment in IS. First, a risk index system with a total of 69 factors is used to assess the degree of fire risk of buildings in IS semi-quantitatively, and the result shows that fire equipment and fire safety management on IS are seriously lacking. Then, a Bayesian network of building fire risk with a total of 66 nodes was established to assess the fire risk from ignition to spread as well as the safety evacuation. Overall, the possibility of ignition is high, but due to the role of fire equipment and fire protection design, the possibilities of fire from ignition to spread is gradually reduced. Finally, we also put forward some feasible suggestions for occupants in IS, community organizations and emergency managers to reduce the fire risk from the aspects of fire equipment and fire safety management.

Rescue is a tough and time-consuming task in huge disaster. After stricken by a catastrophic natural disaster, the rescue force is typically deployed under the circumstance of traffic paralysis and lack of supplies. To cope with this situation, plenty of human resource and professional equipment are required. However, both the human resource and equipment are insufficient in most situations. The rescue force needs an efficient technology for searching the survivals, such as the life detection, which can be a key factor in disaster relief. Fast and accurate locating of the survivals will guide the limited rescue resource to save more survivors. However, current life detection technology cannot meet the demand of rescue in huge disaster. A novel life detection system based on fake base station is proposed. The detection system can scan the whole disaster area and find the cell phones, which leads to the surviving owners. As the system works in different way with other traditional human localization technology, a novel RSS/AOA hybrid positioning algorithm was proposed in this paper to increase the accuracy of localization. To verify the accuracy, two simulation tests were designed. The result of the two tests illustrates that the novel algorithm is more suitable to work with the novel life detection system.

Indoor fire water supply system is an important guarantee for building fire safety, and the design parameters such as water flow and water pressure need to meet the requirements of fire prevention regulations. Based on the monitoring data of the Internet of Things (IoT), this paper took an office building as an example to analyze the water pressure variation trend of the building's indoor fire water supply system, and then predict it based on the historical water pressure drop rate fitting and Long short-term memory (LSTM) method. It is found that the variation of water pressure is periodic, and this trend was analyzed from the perspective of physical structure of the indoor fire water supply system. Furthermore, due to the regularity of water pressure variation, the prediction results are generally good. With historical water pressure drop rate fitting, the prediction accuracy is relatively higher, and the abnormal change points of water pressure can be discovered, but the water pressure value needs to be reset for further prediction; while the LSTM method is self-adaptive, when the frequency of water pressure monitoring is high and the amount of water pressure data is large, the LSTM is more suitable.

In disaster relief, especially in the rescue of serious disaster like great earthquake or tsunami, detecting the exact location of survivors via portable devices can greatly improve the efficiency of search and rescue work. A novel infrastructure-free cell phone positioning system based on software-defined radio, virtual base transceiver station software and its running scheme is proposed in this paper. The identification information of cell phones, receive signal strength and signal transceiver power data can be collected by this system. By using customized antennas with new positioning method, the proposed system can indicate the location of survivals trapped under debris without any operation on cell phones by survivors. With the new hybrid algorithm, search radius of the system can reach more than 1.2 km. And it is also able to implement accurate positioning by this system at the same time. Experimental results demonstrated that the system has significantly improved performance over the traditional methods.

Previous works about deep crowd density estimation usually chose one unified neural network to learn different densities. However, it is hard to train a compact neural network when the crowd density distribution is not uniform in the image. In order to get a compact network, a new method of pre-classification of density to improve the compactness of counting network is proposed in this paper. The method includes two networks: classification neural network and counting neural network. The classification neural network is used to classify crowd density into different classes and each class is fed to its corresponding counting neural networks for training and estimating. To evaluate our method effectively, the experiments are conducted on UCF_CC_50 dataset and Shanghaitech dataset. Comparing with other works, our method achieves a good performance.

Current surveillance system in some specific places like bank, hospital or train station are not efficient enough for city manager or policeman. Huge amount of manpower are needed to make full use of these surveillance data in crime research. In this paper, a novel highly automated surveillance system are proposed. The system would get the identification information and human position indirectly by finding the cellphone they carry on. By running the VBTS (Virtual Base Transceiver Station), IMSI (International Mobile Subscriber Identification Number) of every mobile phone would be collected. VBTS is also able to make cellphones continuously transmit signal. A small sensor is designed to collect the RSS (Receive Signal Strength) and get cellphones' location by triangulation algorithm.

Based on principle of incident chain, the process and relevant techniques of synthetical forecast are studied given business requirement of emergency management. Synthetical forecast is a kind of approach which is supported with GIS, database and artificial intelligence etc. As one of the most important components of construction of emergency platform of public safety, its implementation and improvement is extremely meaningful to manage incidents scientifically and effectively and enhance function of emergency platform.

To find out survivors in some disasters such as earthquake or fire hazard is an extremely important work. The faster search- and-rescue team detect them, the more possibility they could survive. This paper has proposed a new positioning technology based on pseudo base station which designed for searching the survivors under the ruins in earthquake or fire hazard. It could search survivors indirectly by finding the cellphones supposed to be with them. The result of several test shows that the technology could search cellphones in large area in short time. That means this technology will be able to find the survivors under the ruins in disaster area with quickly. That's what is needed urgently for disaster relief work.