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In this paper, aiming at the problem that the on-site rescue personnel can not quickly prepare to perceive the operation damage of the equipment in the disaster damage environment, the invention integrates the machine vision intelligent recognition and wireless sensor acquisition technology and innovatively designs the on-site environment and the equipment perception acquisition method. Firstly, the multi-band point cloud environment map is created by using machine vision method, and the damage state of power equipment is identified from the perspective of emergency disposal to realize the accurate perception and information association of the site environment; secondly, the power field equipment identification based on mobile terminal is studied to realize the identification of the site power grid equipment and related components; finally, the wireless communication modules such as ZigBee are used To realize the fast networking and data acquisition of power equipment sensors. By using augmented reality technology, the real-time operation monitoring data collected by the equipment and the physical equipment are integrated and displayed, and emergency workers can quickly obtain the operation status of the corresponding parts and assist the on-site workers to more accurately and comprehensively view and judge the operation of the whole equipment and parts.

Research and development of multi-information intelligent acquisition module of power grid disaster loss, power grid disaster loss information fusion analysis and prediction module, and multi-person information real-time interaction module. Based on these three modules, a prototype system of power grid disaster intelligent perception and emergency command is developed for the application scenarios of emergency drill, training, and command operation. Develop the integrated interface of the prototype system, realize the software and hardware integration of intelligent mobile terminal, background equipment monitoring system and electric power emergency command platform, and realize the field application verification in the company headquarters and typical electric power emergency drill, rescue and command site of provincial company, realize the intelligent perception of power grid disaster and auxiliary decision-making and visual command under the condition of missing information in the emergency site, so as to realize the real-time monitoring of power grid disaster. It can effectively improve the efficiency of emergency command in disaster judgment, operation command, and material dispatching.

Power system is one of the most fundamental infrastructure systems for human society. The stability of power system greatly affects the economy and security of the city. Therefore, recovering the power system in cities after a severe disaster is the high priority work in a disaster emergency process. Among several kinds of huge disasters, typhoon is a highly valuable research target. Different from the earthquake or tsunami, typhoons often last several days before landing after their generation, so there is much more time for people to prepare and cope with the damage to power system. In this paper, a damage class prediction method for determining the command institution is researched. Based on the actual power system damage data in China from 2004 to 2019, this research proposed the prediction method with machine learning algorithm. The result shows SVM model can reach 77.4 % accuracy in this task, and it is accurate enough to support the emergency decision.

At present domestic emergency information interaction lack of flexibility, after a natural disaster, on-site rescue workers with the rescue headquarters, emergency command center, sharing of information between were studied based on the scene of the electric power emergency mobile information real-time interactive methods, design a kind of power at the scene of the emergency mobile friendly each other, are at the scene of the emergency and emergency command center cluster information real-time interactive technology, gives the power emergency field information real-time interactive system prototype and power at the scene of the emergency information real-time interactive software and hardware integration solutions, to build the efficient method of multi-type information submitted by emergency disposal across departments and information interaction ability.

A shearing-enhanced goethite process for iron removal is proposed to overcome the difficulties in process reaction control and non-uniform mass transfer. The reaction kinetics of the oxidation of Fe2+ to α-FeOOH and crystal growth of α-FeOOH were studied in a ZnSO4-FeSO4 system. Oxidation and crystal growth experiments were performed under the following conditions: shearing speed = 1000–4000 rpm, flow rate of oxygen = 0.3–1.5 L min−1, and reaction temperature = 70–90 °C. The results show that the oxidation rate increases with increasing shearing speed and reaction temperature, owing to the increase in oxygen mass transfer. The degree of crystallization of goethite improves and the needle-like structure is further refined as the shearing speed increases and the temperature decreases. The activation energy of nucleation is about 54 kJ mol−1. The rate equations are derived for the oxidation and oxygen mass transfer processes. The oxidation kinetics parameters are calculated by data fitting. The rate equations of oxygen mass transfer and Fe2+ oxidation are m O 2 = 54.01 k LO ∙ P O 2 H O 2 − C O 2 ∙ M O 2 ∙ v g ∙ n 0.4 and r = 47.08 C Fe 2 + 2 ∙ C O 2 C Fe 2 + + 0.001865 , respectively.

The research of hot-spots is an important subject in public safety field as well as GIS system application, it reveals the spatial distribution law of crime and help develop targeted security preventive strategy, providing safeguards for the whole society. The mass incident is a special kind of public safety incident, but there are little research on its spatial distribution rules from the perspective of geography. This article analyzes the spatial distribution rules of mass incidents in China from the macro point of view and based on the spatial autocorrelation and Kernel density estimation, then finds hot-spots and calculates the degree of spatial agglomeration to express the spatial distribution law of crime.