Analysis of Earthquake Emergency Command System according to Cloud Computing Methods

To solve the problems of slow information acquisition, low processing efficiency, weak information storage and communication ability in earthquake rescue, an earthquake emergency command system on the basis of cloud computing and Internet of Things (IoT) is designed. First, the cloud computing technology is introduced, and the traditional earthquake emergency command system built by cloud computing and the Internet of things is analyzed. Then, based on the satellite remote sensing data, the characteristics of the middle-wave infrared remote sensing data before and after the recent earthquakes in China are explored. Subsequently, a new earthquake emergency command system is built based on cloud computing and Internet of things technology along with the data from the satellite middle-wave infrared remote sensing. Finally, the feasibility of the system is evaluated. The results show that the surface radiation changes significantly before the earthquake, the infrared brightness temperature difference value also fluctuates violently, and the abnormal area will gradually get closer to the epicenter as time goes by. The peak value of the relative power spectrum in the earthquake is more than 9 times of the average value in the normal time. In conclusion, the evaluation result of the emergency command system based on satellite remote sensing data, cloud computing, and Internet of things is good, suggesting satellite infrared remote sensing data can be applied to earthquake prediction, and the earthquake emergency command system constructed combining cloud computing and Internet of things technology has a good feasibility.


I. INTRODUCTION
Earthquake is one of natural disasters.Stronger earthquake has severe destructiveness.Earthquake is caused by plate activity.Generally, the junction of various plate is the place where earthquakes are most likely to occur [1].Because of the vast territory, there are seven earthquake zones in China.Some of the plates in the earthquake zone are not active, while some of the plates are active.Earthquake occur frequently in the areas located in earthquake zone and the periphery [2].According to the statistics, since the 21th century, 8 violent earthquakes at magnitude eight or above have occurred in the world.The death toll in earthquake region of earthquake is numerous, which not only makes the earthquake region suffer heavy economic loss, but also brings permanent disaster and pain to the earthquake region and the people therein [3].In China's "5.12" violent earthquake in 2008, 69,227 people were killed, 17,923 people were missing, 374,643 people were injured, and the economic loss was more than RMB 1 trillion Yuan [4].At present, the ability of informing earthquake in advance is not complete in China.Although technology has developed rapidly since the 21th century, good research achievements in the aspect of earthquake prediction have not been made.With the rapid development of satellite remote sensing technology, infrared data have also been applied to seismic activity research, and satellite infrared remote sensing data have also made new achievements in seismic research [5].In recent years, some progress has been made through applying satellite thermal infrared data to the study of earthquakes cases, and the quantified spatiotemporal evolution feature information has also been obtained, which is of great significance for us to track and obtain the thermal anomalies related to the earthquake with these data, so as to provide a basis for the accurate prediction of the earthquake.
Apart from predicting earthquake in advance to reduce the damage caused by earthquake, earthquake command in case of earthquake can also greatly help to reduce the loss of earthquake region.As the important tool in earthquake emergency, earthquake emergency command system is valued by the country.During the period of the 13th 5-year plan, the government constructed earthquake relief headquarters system in every province, which even covered every county [6].However, in view of the different development levels in various areas in China, there are great gaps between the economics.A lot of times, due to the problem of improper communication, it is impossible to realize the exchange of information among multiple departments and multiple areas when violent earthquakes occur and conduct rescue from all aspects, and it is even impossible to form a site where various departments respond to earthquake coordinately [7][8][9].Therefore, it is an urgent issue to discover a method that can solve the problems of imperfect information resource integration and improper communication between different levels and different departments during earthquake emergency.The emergence of cloud computing and the Internet of things technology has greatly solved the problems of untimely information sharing, mass data storage and incomplete data mining.The application of cloud computing and Internet of things technology in the construction of emergency decision information system can solve the problems of insufficient emergency decision information or blocked information sharing channel.Thus the management problem during earthquake processing is solved, the rescue efficiency in earthquake is improved, and more rescue miracles will be created.
In conclusion, to solve various problems in earthquake emergency rescue command, in the study, on the basis of the characteristics of cloud computing and IoT, cloud computing and IoT technology are employed in the construction of earthquake emergency command system.First, the cloud computing and Internet of things technology are introduced, and the requirements, feasibility, and process of the system are analyzed.Then, based on the satellite infrared remote sensing data, the spatiotemporal characteristics of thermal anomalies of some earthquake cases in China in recent years are explored.Combining satellite infrared remote sensing data with cloud computing and Internet of things technology, the structure and functions of the earthquake emergency command system are designed.Finally, the indexes are established to carry on the feasibility analysis.It is hoped that this study can improve the earthquake rescue ability in China.

A. EARTHQUAKE EMERGENCY COMMAND RESEARCH
The occurrence of earthquake will affect the production of society and people's life and other aspects, and it will also evolve into a social crisis [10].Therefore, the emergency response of earthquake and its management has become an important research content of today's social management, which is also the guarantee of promoting the harmonious development of economy and society.Earthquake emergency command is the core system of earthquake emergency management, which can realize the coordination of all aspects in earthquake rescue operations and promote the effective conduct of rescue operations [11,12].At present, many organizations, experts, and scholars at home and abroad have been trying to construct the earthquake emergency command system that includes various forces, including emergency command system, model, system, and system evaluation.Wang et al. (2019) showed that the earthquake would have a huge impact on the operation of schools.Therefore, based on multivariate data monitoring, they proposed a university earthquake emergency system, in which the decision-making methods include analytic hierarchy process (AHP), linear regression method, earthquake disaster simulation and expert assessment.Finally, it is found that the system can respond more quickly and reasonably [13].Xu et al. (2020) proposed a mapping method of template matching in order to quickly make earthquake emergency map.The method can update the template after an earthquake using the information in the earthquake model to automatically draw an emergency map.Finally, the reliability of this method is verified by case study [14].Ahmadzadeh et al. ( 2019) evaluated the performance of earthquake stress command headquarters in Alborz Medical Sciences University in earthquake accident and disaster risk management, and found that the command headquarters responded timely and could make effective decisions [15].At present, there are many researches on the earthquake emergency system, but most of them focus on the disaster management after the earthquake.

B. RESEARCH STATUS OF CLOUD COMPUTING TECHNOLOGY
Cloud computing is a distributed computing method, which mainly refers to the process of computing and decomposing massive data into numerous small programs through the network "cloud", and then the system composed of multiple servers will analyze and process these small programs, and finally return the processing results to users [16,17].Cloud computing is also being used to build emergency systems, d 'Oro et al. (2019) built a novel architecture based on cloud computing and Internet of things technology to solve the problems of response capacity, bandwidth demand, privacy, and availability in emergency systems.After verification, it is found that the structure of the system has high security and reliability [18].Facchinetti et al. (2019) proposed a software system for indoor emergency response management based on mobile cloud.This system can provide people with escape routes and other help in the event of disasters, greatly improve the reliability and safety of the indoor workplace, and prepare and manage for various emergencies or events [19].Ujjwal et al. (2019) investigated that in the event of earthquake and other disasters, cloud computing and Internet of things technology can be used to more effectively construct natural disaster prediction model and management system [20].

C. APPLICATION OF SATELLITE REMOTE SENSING DATA IN SEISMIC RESEARCH
There have been many researches on the application of satellite remote sensing data in seismic research, but the current research results have not realized practical application in a real sense.Thermal infrared data have great potential in seismic space observation.
Liu et al. (2020) used wavelet transform and relative power spectrum to analyze thermal infrared anomalies of ground earthquakes from MODIS data, and then analyzed the correlation between the area coverage and earthquake amplitude.They showed that weak seismic thermal infrared anomalies can be used to predict earthquakes [21].Barkat et al. (2018) showed that the increase of the earth's surface temperature was closely related to the earthquake.Calculating the increase of the earth's surface temperature using the thermal infrared image of the satellite could obtain the precursor information of the earthquake [22].Wei et al. (2020) used time-frequency, wavelet transform, and relative power spectrum method to analyze the periodic variation rule of satellite characteristic data (including thermal radiation anomaly, thermal radiation background anomaly, and brightness temperature value) [23].Jing et al. (2020) analyzed the anomalous microwave brightness temperature during the strong earthquake in sichuan province of China using the subsection threshold method, and then obtained the anomalous index of microwave radiation, which indicated that the anomalous microwave brightness temperature occurred in the 2 months before the earthquake [24].
To sum up, it is of great significance that the research on the earthquake emergency command system can make the correct decision for different organizations or personnel in the event of an earthquake, while the research on the emergency system based on cloud computing and Internet of things technology can more efficiently and safely complete the emergency management, and seismic prediction based on satellite remote sensing data can improve the reliability of seismic emergency system decision.But at present, the seismic emergency system based on cloud computing technology is still in the shallow level of research, and the importance of each element of the system has not been deeply explored.Therefore, the satellite remote sensing data are used to analyze the thermal infrared anomaly information of some earthquakes in China in recent years, and the technology is applied to the earthquake emergency command system.A seismic emergency command system is built based on cloud computing and Internet of things technology, and the implementation feasibility of this system is evaluated qualitatively and quantitatively.The results of this study are intended to provide theoretical basis for constructing an effective earthquake emergency command system and improving the efficiency of earthquake rescue operations.

A. OVERVIEW OF RELEVANT TECHNOLOGIES
Could computing technology is an information technology generated along with the information technologies in recent years.Through the carrier of Internet, it can provide computing resources including network, computing storage, data and application, conduct computing for users in framework, platform or software without buying various complicated hardware equipment and software.Cloud computing is featured with virtualization, on-demand service, high reliability and dynamic customization, etc. [25].It can be understood as a computing mode extended by parallel, distributed or network computing.Cloud computing can break down massive computing ability of the network into multi-subroutine, distribute them to multiple server to search, compute and analyze data, and feed price back to users.It not only solves various computing problems including computing ability, storage ability and load capacity for users, but also helps users to attain resource sharing and information sharing [26][27][28].
There are three service types of cloud computing: infrastructure as a service, platform as a service and software as a service, and the specific structure is shown in Fig. 1.Among the three service types, the application of cloud computing in earthquake emergency management is mainly infrastructure as a service, which needs to achieve the exchange, update and gathering of information through virtual infrastructure and process various applications coordinately to realize resource integration at the level of information processing [29].

B. ANALYSIS ON THE EARTHQUAKE EMERGENCY COMMAND SYSTEM ON THE BASIS OF CLOUD COMPUTING AND IOT
In the earthquake emergency command system on the basis of cloud computing and IoT, the requirements can be divided into two levels.One is user requirement; another is the functional requirement of system.Fig. 2 shows the business process diagram of the earthquake emergency command system, and the procedure of the whole event is carried out at three stages of before, during and after the event.The stage before the event includes prediction, early warning and information distribution, the stage during the event includes emergency decision and resource allocation, and the stage after the event includes emergency assessment and disaster recovery and reconstruction [30].

FIGURE 2. Business process diagram of the earthquake emergency command system
Fig. 3 shows the decision diagram of traditional earthquake emergency command system, and Fig. 4 shows the decision diagram of the earthquake emergency command system on the basis of cloud computing and IoT.It can be seen from Fig. 3 that, in case of earthquake, if the earthquake parameters input are matched, the system will provide similar earthquake rescue solution according to the existing information; if there's no matching case, the system displays invalid or the solution needs to be improved or changed, which will cost a lot of time and labor.While the earthquake emergency command system on the basis of cloud computing and IoT can provide real-time data at site.Even there's no matching solution, the emergency decision can well adjust decision solution [31].

C. SEISMIC SATELLITE REMOTE SENSING DATA ANALYSIS BASED ON WAVELET DECOMPOSITION
When using satellite remote sensing data for earthquake prediction, it is of great significance to inversely perform the abrupt change of physical parameters such as temperature, so as to identify the abrupt change of bright temperature value during earthquake and then extract the infrared heating anomalies practice, Lipschitz index [32] often used to express the local singularity of the signal.
Supposing n is an integer that is not negative, and 1 n a n    , if there are two constants A and h0(>0), and n degree polynomial Pn(h), any , and When wavelet is used to analyze the local singularity of the signal, the wavelet coefficient depends on the characteristics of f(x) in the x0 domain and the scale of the wavelet transform.In the wavelet transform, if the function f(x) can be represented by and the scale can be represented by S, then equation below is obtained.
Among them, α is the singularity exponent of x0, and K is a constant.If Among them, x0 is the local extremum of the wavelet transform at scale S. and the signal breaks at the maximum of the wavelet transform, so it is necessary to determine an optimal transformation scale.
The equation of wavelet transform is as follows.
Then the earthquake signals are processed based on Multilevel 1D wavelet decomposition.The signal decomposition method is shown in Fig. 5.In this study, middle-wave infrared data product of China's static weather satellite FY-2C/E/G is used.The effective detection range of the satellite is 45°165°E, 60°S~60°N, and observations are made every 1h or 30min, and at least 24 observations are made every day.The file can be uploaded on http://satellite.cma.gov.cn/.In this study, the observation files are obtained from 23:00 to 4:00 Beijing time to avoid direct solar radiation.The converted data are middle wave infrared brightness temperature data, including 5°50°N, 55°150°E.Data is stored in a binary format and the database is built on a yearly basis.Multi-scale wavelet decomposition is used to decompose seismic infrared satellite data, and then the abnormal changes of surface radiation before and after the earthquake are analyzed.

D. SURFACE BRIGHTNESS TEMPERATURE VALUE ANALYSIS BASED ON SATELLITE INFRARED REMOTE SENSING RADIATION
Satellite remote sensing is to collect and record ground object thermal radiation information by satellite or airborne sensors, and to identify ground object and inversion surface parameters using the thermal radiation information.In the analysis, the "blackbody" radiation law needs to be introduced.In this study, the surface brightness temperature Among them, M is the radiation function of the black body; T is the absolute temperature (K); λ is the wavelength; h is Planck's constant (6.626×10-34J• s); c is the speed of light (3×10-8m/s); k is the Boltzmann constant (1.38×10-23J/K).Then the Planck radiation formula is as follows.
  If frequency f is used, it can be expressed as follows.
  In this study, the numerical value conversion of the satellite instrument is first carried out.
Among them, i is the channel number; Ai and Bi are calibration coefficients; Ii is the counting value.
The brightness temperature corresponding to a specific gray value is calculated using the gray-temperature table.Then the brightness temperature value calculated using Planck's formula is as follows.
Among them, TB is the brightness temperature; E is the emissivity after calibration; vis the central wavelength (cm-1).
Affected by the curvature of the earth and atmospheric depletion, the detection data of the infrared channel will vary with the different zenith angles of the observation points.The closer one gets to the edge of the scan line, the longer the detection path, the more severe the atmospheric depletion, the smaller the detection value.The final image is darker than the original, which means the adjacent edge darkens.In order to ensure the authenticity of the collected image, it is necessary to modify it.
Among them, RE is the radius of the earth; H is the altitude of the satellite; σ is the zenith angle of the satellite (which can be obtained by the detection sequence number of the observation point).

E. ESTIMATION OF POWER SPECTRUM BASED ON MIDDLE WAVE INFRARED BRIGHTNESS TEMPERATURE WAVEFORM
Power spectrum estimation is an important part of data signal processing, which can reflect the energy distribution of component power in each frequency of random signal.In this study, the time domain middle wave infrared brightness temperature data processed by wavelet decomposition is used to carry out Fourier transform with 64 days as window length and 1 day as sliding window length, so as to obtain time-frequency spatial data.Then, based on the average power spectrum amplitude, the relative processing of all frequency power spectrum amplitudes in each pixel is carried out.The calculated relative power spectrum amplitude is taken as the abnormal multiple.Image processing technology is used to scan the whole space-time and all frequency bands of the relative power spectrum, and the frequency with a large change in the relative power spectrum amplitude is taken as the dominant frequency and the corresponding period as the characteristic period.When the relative power spectrum value of the abnormal area corresponding to the dominant frequency is more than 6 times, the time of the abnormal area lasts more than 30d, the abnormal area is located at the epicenter or the edge of the earthquake area, the distribution of the abnormal area is related to the direction of the fault, and the time of the occurrence of the abnormal area is related to the time of the earthquake, it can be judged as an earthquake anomaly.
In the database built based on the data collected by fy-2c/E/G, China's static weather satellite, all data are in binary format.The stored pixel is 0.05°×0.05°,so the 0.5°×0.5°region contains a total of 121 /d values, and the daily value in this region is the average value of these data.Therefore, the calculation expression of relative power spectrum in the region of 0.5°×0.5° is as follows.
Among them, in w is the relative power spectrum value of the nth pixel of the id in the region; N=121; 1 365 i  .Then the background value of the relative power spectrum is expressed as follows.
Among them, ink w is the relative power spectrum value of the nth pixel of the kth year id in the region; N=1210; 1 365 i  .Then the equation for calculating the standard deviation of the power spectrum is as follows.

F. DESIGN OF THE EARTHQUAKE EMERGENCY COMMAND SYSTEM ON THE BASIS OF CLOUD COMPUTING AND IOT
The design of the system has six layers, including physical layer, transmission layer, virtual layer, service seal layer, system application layer and user layer, and the specific structure is shown in Fig. 6.Physical layer includes hardware, for example computer equipment, network equipment, IoT sensing equipment, site monitoring and data acquisition end, server, storage equipment and software.Transmission layer is the communication network combined with 3D wireless technology and optical fibre.Virtual layer virtualizes the information required by emergency command such as the data on the basis of GIS (the relative power spectrum and the difference of brightness temperature in satellite infrared remote sensing data based on wavelet decomposition analysis) and monitored data (including video information) to conduct classified management, and constructs virtual pool to realize computing, storage, resource gathering and sharing.Service seal layer is the processing layer that transfers the resources of virtual layer to concrete prediction analysis and decision assistance.System application layer transfers information format through service-oriented architecture, and processes various information businesses in order.User layer connects various emergency parts through standardized service access and invocation interface [33].

Computing resources
Sensor network

PDA C/S Client B/S Client Intelligent Terminal
Screen display

Applications
Resource / process encapsulation Resource / process dynamic matching

Storage device IoT
Operating system Software

Physical resource FIGURE 6. The basic structure of the earthquake emergency command system
The earthquake emergency command system on the basis of cloud computing and IoT is designed with the functions of information management, prediction and early warning, decision assistance, emergency guarantee, command and dispatch and simulation drilling.Information manage function is bale to realize backup, forwarding and processing of earthquake data, as shown in Fig. 7. information management application architecture combines virtualization of cloud computing, to construct storage system with different stratification, as shown in Fig. 8.

A. WAVELET ANALYSIS RESULTS OF SATELLITE REMOTE SENSING DATA
The magnitude-6.8earthquake occurred on August 25, 2008 in Zhongba, Tibet autonomous region, with an epicenter of 31.0 ° N and 83.6 ° E.Then, the observation data of thermal infrared brightness temperature in the region directly under the jurisdiction of no earthquake in this period are used as the normal control, and then the seismic data of Zhongba, Tibet are compared and analyzed.The observation time is from January 1, 2008 to December 1, 2008.Fig. 9 (a) shows the thermal infrared satellite observation data within a year of 28.4 ° n and 102.1 ° e of Sichuan; Fig. 9 (d) shows the thermal infrared satellite observation data within a year of 29.5 ° n and 82.7 ° e of Zhongba, Tibet.It can be seen from Fig. 9(a) that the variation trend of the bright temperature value of the thermal infrared satellite in the area without earthquake is like a sinusoidal curve with noise, but the overall change has a certain rule.In Fig. 9(d), there is an abnormally increasing trend of the brightness temperature value of thermal infrared radiation in the 3 months before August 2008.
Therefore, wavelet decomposition is used for signal analysis.

B. ANALYSIS OF TEMPERATURE DIFFERENCE OF SATELLITE THERMAL INFRARED BEFORE AND AFTER EARTHQUAKE
The In the first month before the earthquake, the abnormal area gradually appears, and with the passage of time, the abnormal area gradually approaches the epicenter, and reaches the maximum area in the month of the earthquake, and gradually decreases after the earthquake.The whole shows a trend of strengthening and then weakening, and then disappearing.Then, the time series analysis of relative power spectrum in the region of 0.5°×0.5°near the epicenter of each region is carried out.From Fig. 12(a), Fig. 12(b), Fig. 12(c), and Fig. 12(d), it can be found that there are obvious abnormal changes on the time series curve of each region within one year of the earthquake, and the abnormal values reach the peak within one week of the earthquake, which are 8 to 16 times of the average values, respectively.The time of average amplitude more than 2 times the average lasts for about 30 days.At the same time, before and after the earthquake, the difference that regional time series curve deviates from the background value and the standard deviation time series curve also increases, especially the peak of the power spectrum deviates the most during the earthquake.As can be concluded from Table I, among the abnormal features of the relative power spectrum of the infrared middle wave in the earthquake, the peaks are mainly more than 9 times of the normal, and the peak occurs more often before the earthquake, and the average duration greater than 2 times is within 30d~70d.VOLUME XX, 2017

D. OUTPUT DESIGN OF EARTHQUAKE EMERGENCY COMMAND SYSTEM
Based on the needs of users and various functions in the system module, the design of the output interface of the system in this study includes user login, function selection, information management, prediction and warning, command and dispatch, and simulation exercise, etc.The output system interface is shown in Fig. 13.

E. INDEX CONSTRUCTION OF THE EARTHQUAKE EMERGENCY COMMAND SYSTEM ON THE BASIS OF CLOUD COMPUTING AND IOT
In order to make the earthquake emergency command system on the basis of cloud computing and IoT more comprehensive and precise in the evaluation of index construction, the analysis method from multiple angles in which departments involved in earthquake emergency command system is not considered, and only a set of earthquake emergency command system on the basis of the new generation of technology from the perspective of the user of the system-earthquake emergency department is built [34].5 first class indexes and 15 second class indexes are constructed according to the necessity and possibility of earthquake emergency system, as shown in Fig. 14, and the description for the function of each index is shown in Fig. 14.The perfection of the system of earthquake emergency department Weight of index is the importance and contribution of the various indexes in a system for the system [35].There are many computing methods of weight, and analytic hierarchy process (AHP) is adopted in the study, which breaks down the index to the levels of goal, criteria and solution and conducts quantitative or qualitative analysis on the basis of this [36].
AHP includes four steps: Firstly, the problems that requires decision making is broke down according to different goals or criteria, and earthquake emergency command system can be divided into four different layers-target layer, criteria layer, index layer and solution layer on the basis of the study.Secondly, judgement matrix needs to be built, and judgement matrix compares and judges the importance of evaluation according to evaluation index.Thirdly, the maximum eigenvalue of judgement matrix is calculated.Fourthly, consistency check is conducted to judgement matrix [37], and the equation of consistency check is: The calculation method for average random consistency index (RI) is as follows.For a fixed a comparison matrix A is randomly constructed, and aij is randomly extracted from 1, 2, ..., 9, 1/2, 1/3, ..., 1/9.Such A is inconsistent, a sufficiently large A is taken to obtain an average of the maximum eigenvalues of the matrix A. According to referring to related references, RI is compared with the comparison matrix, it is found that, RI is average random consistency index, and only n is its impact index [38].The calculation method of consistency ratio is shown as equation (17): Consistency ratio is compared with CR to judgement.When CR<0.1, the system has favored consistency with the comparison matrix, or the result is acceptable; when CR>0.1, the system is not acceptable or favored by the comparison matrix, and it is necessary to the adjust the judgement matrix until the consistency is met [39].Contrast scale table is shown in Table IV.When confirming the weight of rating system index, it is necessary to calculate the eigenvector W and the maximum eigenvalue max  of each questionnaire with summation method, and the first step of the calculation procedure is normalization processing the vectors in each column of comparison matrix [40] Then it is the summation of each line of , , 1, 2,3, , Next, it is the normalization of C, and the following equation is obtained: At last, the maximum eigenvalue is calculated through equation ( 8): In which, ( ) i AW is the i-th component of AW.
The average weights of first class indexes are calculated according to the above procedures, as shown in Table IV.When confirming the weight of feasibility evaluation index, 20 persons from earthquake emergency department are selected to evaluate the importance of each index.Comparison matrix is built according to the evaluation of 20 persons, computing and consistency check are carried out according to the procedures of AHP, and the weights that pass consistency check are acceptable [41].In the end, arithmetic mean value is conducted to the weights of various indexes, as shown in Table V.Table VI shows the weights of the evaluation system of earthquake emergency command system.It is known from Table VI that, earthquake emergency personnel focus on index AI in the first class indexes.They concern a11 mostly in the first class index, which is system functional integrity; they concern indexes A4 and A5 the least.They focus on a13 the least in second class indexes, which is system process matching degree.

F. EVALUATION RESULT OF IMPLEMENTATION FEASIBILITY OF THE EARTHQUAKE COMMAND SYSTEM ON THE OF CLOUD COMPUTING AND IOT
When evaluating the implementation feasibility of the earthquake emergency command system on the basis of cloud computing and IoT, 20 experts from earthquake emergency departments are chosen to carry on the test of various indexes, and the results of test are shown in Table VII.Evaluation grade set V = (7,5,3,1), wherein the items represent excellent, better, good and poor evaluation grades respectively, and comprehensive grade evaluation of second indexes can be represented with equation ( 9): 0.55 0.25 0.20 0.00 0.60 0.15 0.16 0.10 (0.30, 0.23, 0.09, 0.21, 0.17) 0.45 0.30 0.20 0.05 0.50 0.29 0.10 0.10 0.52, 0.22, 0.17, 0.09 It is known from Table II that, 51.9% respondents think that the earthquake emergency command system is designed with good feasibility, 8.1% respondents think that the feasibility is poor, and 41% respondents have ambiguous attitudes.The above result is changed to value with equation (25): T 1 1 (0.52,0.22,0.17,0.09)(7,5,3,1) 5.43 (0.71, 0.12, 0.08, 0.09), 5.7 (0.51, 0.17, 0.
At last, the score obtained is 5.36, and the total score of the earthquake emergency command system is higher than that in better grade, hence it can be determined that the system has good feasibility.

V. CONCLUSION
The study is aimed to solve the various problems in earthquake rescue, cloud computing and IoT are applied in the design of earthquake emergency command system.Based on satellite infrared remote sensing data, first, the change characteristics of surface radiation, brightness temperature difference, and relative power spectrum before and after the occurrence of some earthquakes in China in recent years are analyzed.The data rule before the earthquake is found to provide the basis for the accurate prediction of the earthquake.Then, based on satellite infrared remote sensing data, an earthquake emergency system of cloud computing and Internet of things is constructed and explored.AHP is conducted to the system from the perspective of earthquake emergency department, it is concluded according to fuzzy rating that the system has better grade score no matter from the elements of technology, economy and system, hence it reveals that the system has certain feasibility.
Although the study provides better development direction for earthquake command system, earthquake has complexity and there are various problems in earthquake rescue, there may be limitation in the realization of the functions, more and better functions of the system are to be imagined and developed.

FIGURE 1 .
FIGURE 1.The basic architecture of cloud computing

FIGURE 3 .
FIGURE 3. Decision diagram of traditional earthquake emergency command system

FIGURE 4 .
FIGURE 4. Decision diagram of the earthquake emergency command system on the basis of cloud computing and IoT scaling and translation transformation of the function   t  ; a is the scaling factor; B is the translation factor.Given a square-integrable signal a   X t , then equation below is obtained.

FIGURE 5 .
FIGURE 5. Schematic diagram of multi-scale wavelet decomposition This work is licensed under a Creative Commons Attribution 4.0 License.For more information, see https://creativecommons.org/licenses/by/4.0/.This article has been accepted for publication in a future issue of this journal, but has not been fully edited.Content may change prior to final publication.Citation information: DOI 10.1109/ACCESS.2020.3019833,IEEE Access Author Name: Preparation of Papers for IEEE Access (February 2017) 6 VOLUME XX, 2017value is calculated based on the Planck radiation law.
observation points can be calculated by satellite zenith angle  .

FIGURE 7 .FIGURE 8 .
FIGURE 7. Structure diagram of the information management system of the earthquake emergency command system on the basis of cloud computing and IoT

Fig. 9 (
b) and Fig. 9 (c), respectively represent the high frequency and low frequency components in the area of 28.4 ° n and 102.1 ° e of Sichuan, while Fig. 9 (d) and Fig. 9 (e) represent the high frequency and low frequency components in the area of 29.5 ° n and 82.7 ° e of Zhongba, Tibet, respectively.As can be concluded from Fig. 9 (b), although the high-frequency components in the areas without earthquakes in Sichuan province are in a small range of mutation, they are evenly distributed on the whole.As can be concluded from Fig. 9(c), the variation trend of the low-frequency component (thermal infrared bright temperature value) in this region is similar to that of the sinusoidal signal, and has obvious periodicity, which conforms to the annual variation rule of the surface temperature.As can be concluded from Fig. 9 (d), an obvious mutation occurs in Zhongba county in Tibet before the earthquake (around August 2008).As can be concluded from Fig. 9(e), the low-frequency component (thermal infrared bright temperature value) after wavelet decomposition shows an obvious rising trend from June 2008 to August 2008, and then reaches the bottom rapidly, indicating that the surface radiation in Zhongba county in Tibet changes significantly before the earthquake.

FIGURE 9 .
FIGURE 9.The variation trend of thermal radiation and the results of wavelet decomposition from January 1, 2008 to December 1, 2008 in regions with no earthquake in Sichuan and Zhongba, Tibet satellite observation data of Ms8.0 magnitude earthquake (epicenter 31 ° N, 103.4 ° E, focal depth 33km) that occurred in Wenchuan, Sichuan province on May 12, 2008 (epicenter 31 ° N, 103.4 ° E, focal depth 33km), Ms6.8 magnitude earthquake (epicenter 31 ° N, 83.6 ° E, focal depth 10km) that occurred in Zhongba, Tibet on August 25, 2008 (epicenter 31 ° N, 83.6 ° E, focal depth 10km), Ms6.6 magnitude earthquake (epicenter 44.27 ° N, 82.89 ° E, focal depth of 11km), and Ms5.7 earthquake (epicenter: 45.27 ° N, 124.71 ° E, focal depth: 13km) occurred in Songyuan, Jilin province on May 28, 2018 are used for infrared temperature differences analysis.From Fig. 10 (a), Fig. 10 (b), Fig. 10 (c), and Fig. 10 (d), it can be concluded that 1 year before and after the earthquake in Wenchuan of Sichuan province, Zhongba in Tibet, Jinghe of Xinjiang province and Songyuan of Jilin province, the satellite thermal infrared has been from weak to strong, and rapidly decreased after the earthquake.However, the brightness temperature difference First, the seismic area is scanned for half a year.It can be concluded from Fig. 11(a), Fig. 11(b), Fig. 11(c), and Fig. 11(d) that the relative power spectrum can well reflect the information of infrared anomalies in seismic waves.

FIGURE 11 .
FIGURE 11.Spatiotemporal variation of relative infrared power spectrum of earthquake middle waves in different regions.Note: Fig.(a) shows the spatiotemporal variation of the relative

FIGURE 12 .
FIGURE 12.The time series curve of relative middle wave infrared power spectrum in each region within one year.Note: Fig.(a) is the timing sequence curve of the relative infrared power spectrum of the mid-wave earthquakes in Wenchuan, Sichuan province from January 1, 2008 to December 1, 2008.Fig.(b) is the time series curve of the relative infrared power spectrum of the mid-wave earthquakes in Zhongba, Tibet from January 1, 2008 to December 1, 2008.Fig. (c) is the time series curve of relative infrared power spectrum of the earthquake middle wave from January 1, 2017 to December 1, 2017 in Jinghe, Xinjiang.Fig.(d) is the time series curve of relative infrared power spectrum of the mid-wave earthquakes in Songyuan, Jilin province from January 1, 2011 to December 1, 2011

FIGURE 13 .
FIGURE 13.Display of output interface of earthquake emergency command system

FIGURE 14 .
FIGURE 14.First indexes and second indexes of the system

TABLE I ANALYSIS
OF ABNORMAL CHARACTERISTICS OF MIDDLE WAVE INFRARED RELATIVE POWER SPECTRUM IN EARTHQUAKE

TABLE II .
DESCRIPTION OF SYSTEM EVALUATION INDEXThis work is licensed under a Creative Commons Attribution 4.0 License.For more information, see https://creativecommons.org/licenses/by/4.0/.This article has been accepted for publication in a future issue of this journal, but has not been fully edited.Content may change prior to final publication.Citation information: DOI 10.1109/ACCESS.2020.3019833,IEEEAccessAuthor Name: Preparation of Papers for IEEE Access (February 2017)

TABLE VII .
TEST RESULTS OF FIRST CLASS INDEXES AND SECOND INDEXES OF