CN113780902B - Disaster risk early warning management system based on cloud computing - Google Patents
Disaster risk early warning management system based on cloud computing Download PDFInfo
- Publication number
- CN113780902B CN113780902B CN202111336182.6A CN202111336182A CN113780902B CN 113780902 B CN113780902 B CN 113780902B CN 202111336182 A CN202111336182 A CN 202111336182A CN 113780902 B CN113780902 B CN 113780902B
- Authority
- CN
- China
- Prior art keywords
- module
- risk
- data
- disaster
- density
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000011156 evaluation Methods 0.000 claims abstract description 54
- 238000012544 monitoring process Methods 0.000 claims abstract description 43
- 238000012545 processing Methods 0.000 claims abstract description 39
- 238000004364 calculation method Methods 0.000 claims abstract description 32
- 238000004458 analytical method Methods 0.000 claims abstract description 20
- 238000011160 research Methods 0.000 claims abstract description 14
- 238000009430 construction management Methods 0.000 claims abstract description 8
- 239000011159 matrix material Substances 0.000 claims description 67
- 238000004891 communication Methods 0.000 claims description 27
- 238000012502 risk assessment Methods 0.000 claims description 20
- 230000005540 biological transmission Effects 0.000 claims description 18
- 239000013598 vector Substances 0.000 claims description 17
- 238000000034 method Methods 0.000 claims description 15
- 238000005516 engineering process Methods 0.000 claims description 14
- 238000010276 construction Methods 0.000 claims description 11
- 238000004422 calculation algorithm Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 6
- 230000007613 environmental effect Effects 0.000 claims description 6
- 238000013178 mathematical model Methods 0.000 claims description 6
- 230000015572 biosynthetic process Effects 0.000 claims description 5
- 238000003696 structure analysis method Methods 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 4
- 238000007689 inspection Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000011084 recovery Methods 0.000 claims description 3
- 230000002194 synthesizing effect Effects 0.000 claims description 2
- 238000001514 detection method Methods 0.000 claims 1
- 230000002265 prevention Effects 0.000 abstract description 2
- 238000013461 design Methods 0.000 abstract 1
- 230000006872 improvement Effects 0.000 description 9
- 238000010586 diagram Methods 0.000 description 4
- BTCSSZJGUNDROE-UHFFFAOYSA-N gamma-aminobutyric acid Chemical compound NCCCC(O)=O BTCSSZJGUNDROE-UHFFFAOYSA-N 0.000 description 4
- 238000011161 development Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012806 monitoring device Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010205 computational analysis Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000005180 public health Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000010972 statistical evaluation Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of risk management, in particular to a disaster risk early warning management system based on cloud computing. The system comprises a capital construction management unit, a data processing unit, a risk research unit and an early warning management unit; the data processing unit is used for processing the monitoring data; the risk research unit is used for carrying out calculation evaluation on the risk level of the disaster based on cloud calculation; and the early warning management unit is used for displaying the analysis result and carrying out early warning. The design of the invention can acquire various meteorological target state data in real time and classify and count the meteorological target state data, thereby realizing the identification, evaluation and classification of disaster risks; the damage degree of the natural disaster to the area can be more accurately evaluated; the disaster risk prediction condition is displayed in a multivariate way, and the predicted disaster risk can be fed back to the public in various ways, so that emergency treatment and prevention arrangement can be carried out in time, and the casualties and economic loss conditions possibly caused by disasters can be reduced.
Description
Technical Field
The invention relates to the technical field of risk management, in particular to a disaster risk early warning management system based on cloud computing.
Background
The disasters refer to phenomena of damage to human survival and social development, personal casualties, property loss and other adverse consequences caused by natural, artificial or natural and artificial comprehensive reasons, and are mainly divided into four categories, namely natural disasters, accident disasters, public health practices, social security events and the like. The disaster mainly comprises a pregnant disaster environment, a disaster causing factor, a bearing body and a disaster situation. With the continuous development of scientific technology, natural meteorological conditions can be predicted more and more accurately, and if the method is based on accurate meteorological monitoring and combined with the pregnant disaster environment and the collision analysis of disaster-causing factors, the method is expected to predict various natural disasters possibly caused by the meteorological conditions, such as heavy rainfall/snow, drought and waterlogging, extreme high and low temperature weather and other disasters, and can inform the public in time so as to rapidly perform emergency treatment, thereby reducing casualties, economic losses and the like. However, at present, there is no comprehensive and accurate disaster risk early warning management system based on cloud computing.
Disclosure of Invention
The invention aims to provide a disaster risk early warning management system based on cloud computing to solve the problems in the background technology.
In order to solve the above technical problems, an object of the present invention is to provide a disaster risk early warning management system based on cloud computing, which includes
The system comprises a capital construction management unit, a data processing unit, a risk research unit and an early warning management unit; the capital construction management unit, the data processing unit, the risk research unit and the early warning management unit are sequentially connected through network communication; the infrastructure management unit is used for managing and controlling infrastructure operated by the early warning management system, and the infrastructure comprises equipment, a sensing device, a database and a communication technology; the data processing unit is used for clustering and counting the monitoring data acquired by each target monitoring terminal; the risk research unit is used for constructing a hierarchical model of disaster risks and carrying out calculation and evaluation on the risk level of the disaster based on cloud calculation; the early warning management unit is used for displaying the analysis result in a multivariate mode and carrying out early warning management on the disaster risk;
the infrastructure management unit comprises a basic equipment module, a monitoring and sensing module, a distribution database module and a communication support module;
the data processing unit comprises an acquisition and transmission module, a density clustering module, a secondary clustering module and a classification and statistics module;
the risk research unit comprises a hierarchical model module, a judgment and calculation module, an evaluation grading module and a risk evaluation module;
the early warning management unit comprises a report form graphic module, a comprehensive report module, a multivariate display module and a risk warning module.
As a further improvement of the technical scheme, the basic device module, the monitoring and sensing module, the distribution database module and the communication support module are sequentially connected through network communication; the basic equipment module is used for providing and managing processing equipment for constituting system operation; the monitoring sensing module is used for distributing a plurality of meteorological environment target monitoring terminal sensors which can form natural disasters in a monitoring area and acquiring corresponding state data acquired by each sensor in real time; the distributed database module is used for establishing a distributed database at the cloud based on a block chain technology so as to provide data service at the near end of the terminal; the communication support module is used for establishing signal connection and data transmission channels among all layers of the system and all devices in the system through various communication technologies; the distributed database module performs data synchronization by adopting a main database and standby database mode, wherein the distributed database module specifically comprises: judging whether the number of the main and standby data files is consistent, and if not, retransmitting the latest control file; checking the log range of the main library and comparing the change number of the control file system of the standby library, and retransmitting the latest control file if the control file of the standby library is smaller than the log range; after the control file is retransmitted, updating data file information corresponding to the control file; detecting whether the backup database data file is in the range of the main database log; retransmitting the data files with broken gears and the data files which are not sent, and updating the information of the control files; executing one-time log archiving, recording the first change number of the last record, and determining an archiving log to be transmitted; transmitting the archive log of the differences from the primary repository to the backup repository; performing data recovery on the standby database to ensure that all data files are transmitted completely and the log is applied to the latest state; and re-opening the standby database, verifying whether the database can be normally opened, and checking whether the data file has transmission errors.
The basic device includes, but is not limited to, a computer, a processor, a display terminal, a mobile terminal, etc.
The target monitoring terminal sensor includes, but is not limited to, a wind anemometer, a weather thermometer, a lightning monitoring device, a lightning current on-line monitoring instrument, a weather hydrological monitor, a rainfall monitor, and the like.
The communication technology includes, but is not limited to, wired network, local area network, wireless transmission, data traffic, bluetooth, ZigBee, and the like.
As a further improvement of the technical solution, a signal output end of the acquisition and transmission module is connected with a signal input end of the density clustering module, a signal output end of the density clustering module is connected with a signal input end of the secondary clustering module, and a signal output end of the secondary clustering module is connected with a signal input end of the classification and statistics module; the acquisition and transmission module is used for acquiring corresponding state data through the distributed target monitoring terminals and transmitting the state data to the data processing unit of the system in real time; the density clustering module is used for randomly extracting partial sample data from the data statistically processed by the data processing unit, performing density-based clustering processing on the sampled data, and rapidly clustering to determine the cluster number and the initial cluster center; the secondary clustering module is used for performing fast clustering processing on the data statistically processed by the data processing unit by taking the cluster number and the initial cluster center obtained by sample density clustering as input conditions; and the classification and statistics module is used for classifying, counting and storing the data according to the target type of the detected data according to the result of the rapid clustering so as to perform collision analysis on the data with correlation in the following process.
As a further improvement of the technical solution, in the density clustering module, a DBSCAN algorithm is preferably used for the density clustering method, and the specific steps of the algorithm are as follows:
step1, with each data point x i As a circle center, drawing a circle with eps as a radius, the circle being called x i An eps neighborhood of;
step2, calculating points contained in the circle, if the number of the points in the circle exceeds a density threshold MinPts, marking the circle center of the circle as a core point, also called a core object, if the number of the points in the eps neighborhood of a certain point is smaller than the density threshold, and only falls in the neighborhood of the core point, then the point is called a boundary point, and meanwhile, the points which are not the core point nor the boundary point are called noise points or outliers; according to the eps neighborhood and the density threshold MinPts, sequentially finishing the point type judgment of all data, and deleting noise points or outliers; core point x i All points in the neighborhood of eps are x i Direct density of (d) is not direct, if x j From x i Direct density, x k From x j Direct density, once, x n From x k Direct density, then the transmissibility of direct density can be deduced, x n From x i The density can be reached; if for x k Let x be i And x j All can be formed by x k The density can be reached, then x can be called i And x j Density connected, connecting the density connected points to form cluster；
Step3, connecting two core points with the distance less than MinPts together to form a plurality of clusters; the boundary points are assigned to the nearest core point range until the final clustering result is formed.
As a further improvement of the technical solution, a signal output end of the hierarchical model module is connected with a signal input end of the judgment and calculation module, a signal output end of the judgment and calculation module is connected with a signal input end of the evaluation and classification module, and a signal output end of the evaluation and classification module is connected with a signal input end of the risk assessment module; the hierarchical model module is used for selecting a plurality of indexes to carry out normalization processing respectively according to the influence possibly caused by disaster risks in an area, taking each index as an initial value of disaster risk evaluation, and establishing a hierarchical structure model of risk evaluation according to risk evaluation requirements; the judgment calculation module is used for constructing a judgment matrix of each risk index factor and acquiring the weight value of each index in the comprehensive risk degree evaluation through the analysis calculation of the matrix; the evaluation grading module is used for calculating a weight matrix of each index by combining a hierarchical structure analysis method, establishing a mathematical model of a disaster risk degree evaluation index by combining risk evaluation indexes, and dividing the risk degree of the disaster in the area into different grades according to the indexes according to a certain evaluation standard; the risk evaluation module is used for carrying out statistical evaluation and analysis on different types of disasters and risk degrees respectively by combining a large number of environmental conditions in an area.
The hierarchical structure model can be divided into three or more layers, and here, a three-layer structure is preferred, and specifically, the hierarchical structure model can be: the first layer is a target layer of disaster risk degree in the area, the second layer is a criterion layer of disaster forming conditions, and the third layer is an index layer of comprehensive influence factors; specifically, the evaluation index includes, but is not limited to, the frequency of occurrence of disaster in a region, the density of regional network, the environmental condition of the region, the distribution density of population in the region, and the like.
As a further improvement of the technical scheme, the judgment and calculation module comprises a construction matrix module, a level single ordering module, a level total ordering module and a feature vector module; the signal output end of the construction matrix module is connected with the signal input end of the hierarchical single sorting module, the signal output end of the hierarchical single sorting module is connected with the signal input end of the hierarchical total sorting module, and the signal output end of the hierarchical total sorting module is connected with the signal input end of the feature vector module; the construction matrix module is used for establishing a judgment matrix of a paired comparison matrix by pairwise comparison of factors when the factors influencing the disaster formation are more, so that the relationship among the factors in the hierarchical structure can be reflected more intuitively; the hierarchical single ordering module is used for carrying out consistency check on the constructed judgment matrix so as to check the rationality of the matrix and the weight vector derived from the matrix, and obtaining the weight vector of a group of elements to a certain element in the upper layer; the hierarchical total sorting module is used for synthesizing the weights under the single criterion from top to bottom to obtain the total sorting weight and carrying out consistency check on the hierarchical total sorting from a high layer to a low layer; and the characteristic vector module is used for calculating the characteristic vectors in the judgment matrix to serve as the weight of each index in the comprehensive risk degree evaluation.
As a further improvement of the technical solution, the construction matrix module establishes a pairwise comparison matrix to compare the factors pairwise by using an analytic hierarchy process principle, specifically:
if matrix
A＝(a ij ) n×n
Satisfies a ij ＞0，a ji ＝1/a ij ，
It is called a positive reciprocal matrix, where: i, j ═ 1, 2,. ·, n; n is the number of rows and columns of the matrix;
wherein, a ij The values of (c) can be scaled by the numbers 1-9 and their inverse to indicate the degree of importance between the two factors.
Wherein, the specific meanings of the numbers 1-9 and the reciprocal thereof as the scale are as follows: 1 indicates that two factors are of equal importance, 3 indicates that two factors are slightly more important than the former, 5 indicates that two factors are significantly more important than the latter, 7 indicates that two factors are strongly more important than the former, and 9 indicates that two factors areThe values of 2, 4, 6 and 8 represent intermediate values of the above adjacent judgment respectively, and the reciprocal means that if the ratio of the importance of the factor i to the importance of the factor j is a ij The ratio of the importance of the factor j to the factor i is 1/a ij 。
The reason for constructing the judgment matrix is as follows: the hierarchical structure only reflects the relationship among all factors, the proportion of all the criteria in the single criterion layer in the target measurement is not necessarily the same, and when the factors of image disaster formation are more, the influence of all the factors on the disaster is directly considered, and the factors are probably lost due to incomplete consideration.
As a further improvement of the technical scheme, consistency check in the hierarchical single ordering module and the hierarchical total ordering module adopts a consistency ratio index C R And (3) carrying out inspection, which specifically comprises the following steps:
firstly, the consistency index C is calculated I ：
Wherein λ is max Is the maximum eigenvalue of the matrix, and n is the number of factors;
then determining corresponding average random consistency index R by table look-up I ；
Then, the consistency ratio C is calculated R And judging:
when C is present R When < 0.1, the consistency of the judgment matrix is considered to be acceptable, and when C is R And when the judgment matrix is more than 0.1, the judgment matrix is considered not to meet the consistency requirement, and the judgment matrix needs to be revised again.
After the weight vector is calculated according to the judgment matrix, the judgment matrix can be logically reasonable only through consistency check, and the result can be continuously analyzed.
As a further improvement of the technical solution, in the evaluation grading module, a mathematical model of an evaluation index of the disaster risk degree in the area is as follows:
wherein, w is the index of risk degree, m is the total number of evaluation indexes of risk degree, R (j) is the weighted value of each evaluation index, and Y (j) is the normalized index value of each index.
As a further improvement of the technical scheme, the report form graphic module, the comprehensive report module, the multivariate display module and the risk warning module are sequentially connected through network communication; the report form graphic module is used for drawing the data obtained by statistics, calculation and analysis into a corresponding table or a corresponding statistical graph in real time through drawing software loaded in the early warning management system; the comprehensive report module is used for generating a comprehensive disaster risk assessment early warning report by combining data information and a drawn graph; the multi-element display module is used for displaying the disaster risk condition of calculation analysis on the display terminal in real time through a plurality of structural modes or interface forms; the risk warning module is used for sending out warning in various modes when certain meteorological factors are monitored to reach a certain disaster risk degree.
The risk warning mode comprises a flashing screen, a popup window, a warning sound, a short message notification and the like.
The invention also aims to provide an operation method of the disaster risk early warning management system based on cloud computing, which comprises the following steps:
firstly, various target monitoring terminals arranged at each position in a monitoring area are used for acquiring corresponding target state data in real time and transmitting the data to a data processing unit, performing density clustering and rapid secondary clustering operation on a large amount of data, classifying, counting and storing the data according to different monitoring targets, secondly, target data belonging to the same category are imported into a hierarchical model, the influence degree of each index factor on disaster risk is judged and calculated through a hierarchical structure analysis method, the evaluation weight value of each index factor is calculated according to the influence degree, and respectively carry out evaluation, sorting and grading operation on the disaster risk degree caused by each monitoring target, and then, drawing the real-time calculation and analysis result into an intuitive chart, automatically generating a corresponding comprehensive report for reporting, storing and managing, and finally feeding back the disaster risk predicted by analysis and the related information to public users according to a preset alarm mode.
Compared with the prior art, the invention has the beneficial effects that:
1. the disaster risk early warning management system based on cloud computing is based on a mature meteorological prediction technology, various meteorological target monitoring terminal sensors are distributed in a monitoring area, various meteorological target state data can be collected and uploaded in real time, classification and statistics of the data can be completed quickly by performing quick secondary clustering processing on the data, and identification, evaluation and classification of disaster risks are realized by combining a hierarchical structure model;
2. according to the disaster risk early warning management system based on cloud computing, the damage degree of natural disasters to an area can be more accurately judged by analyzing the pregnant disaster environment and disaster-causing factors of natural meteorology and combining factors such as environmental conditions and population density distribution in the area, so that accurate disaster risk assessment is realized;
3. the disaster risk early warning management system based on cloud computing displays disaster risk prediction conditions in a diversified and real-time manner through various forms such as graphic reports and comprehensive reports, and can feed the predicted disaster risks back to the public through various manners, so that emergency treatment and prevention arrangement can be performed in time, and casualties and economic loss conditions possibly caused by disasters can be relieved.
Drawings
FIG. 1 is an overall exemplary product architecture diagram of the present invention;
FIG. 2 is a block diagram of the overall system of the present invention;
FIG. 3 is one of the partial system architecture diagrams of the present invention;
FIG. 4 is a second partial system architecture of the present invention;
FIG. 5 is a third diagram of a partial system architecture according to the present invention;
FIG. 6 is a fourth embodiment of the present invention;
FIG. 7 is a fifth embodiment of the present invention.
The various reference numbers in the figures mean:
1. a computer processor; 2. calculating a model; 3. a target monitoring sensor; 4. a cloud database; 5. displaying a large screen; 6. a mobile terminal; 7. a user;
100. a capital construction management unit; 101. a base equipment module; 102. monitoring a sensing module; 103. a distribution database module; 104. a communication support module;
200. a data processing unit; 201. a collection transmission module; 202. a density clustering module; 203. a secondary clustering module; 204. a classification statistical module;
300. a risk study unit; 301. a hierarchical model module; 302. a judgment calculation module; 3021. constructing a matrix module; 3022. a hierarchical single-sequencing module; 3023. a total hierarchical ranking module; 3024. a feature vector module; 303. an evaluation grading module; 304. a risk assessment module;
400. an early warning management unit; 401. a report form graphic module; 402. a comprehensive report module; 403. a plurality of display modules; 404. and a risk warning module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1 to 7, the present embodiment provides a disaster risk early warning management system based on cloud computing, which includes
The system comprises a capital construction management unit 100, a data processing unit 200, a risk research unit 300 and an early warning management unit 400; the infrastructure management unit 100, the data processing unit 200, the risk research unit 300 and the early warning management unit 400 are sequentially connected through network communication; the infrastructure management unit 100 is configured to manage and control infrastructure of the operation of the early warning management system, where the infrastructure includes devices, sensing devices, databases, and communication technologies; the data processing unit 200 is configured to perform clustering and statistical processing on the monitoring data acquired by each target monitoring terminal; the risk research unit 300 is used for constructing a hierarchical model of disaster risks and performing calculation and evaluation on the risk level of the disaster based on cloud calculation; the early warning management unit 400 is configured to display an analysis result in a multivariate manner and perform early warning management on disaster risks;
the infrastructure management unit 100 comprises a basic equipment module 101, a monitoring and sensing module 102, a distribution database module 103 and a communication support module 104;
the data processing unit 200 comprises an acquisition and transmission module 201, a density clustering module 202, a secondary clustering module 203 and a classification and statistics module 204;
the risk research unit 300 comprises a hierarchical model module 301, a judgment calculation module 302, an evaluation grading module 303 and a risk assessment module 304;
the early warning management unit 400 comprises a report form graphic module 401, a comprehensive report module 402, a multivariate display module 403 and a risk warning module 404.
In this embodiment, the basic device module 101, the monitoring and sensing module 102, the distribution database module 103 and the communication support module 104 are sequentially connected through network communication; the basic device module 101 is used for providing and managing processing devices which form the operation of the system; the monitoring sensing module 102 is used for distributing a plurality of meteorological environment target monitoring terminal sensors which may form natural disasters in a monitoring area and acquiring corresponding state data acquired by each sensor in real time; the distributed database module 103 is used for establishing a distributed database at the cloud end based on a block chain technology so as to provide data services at the terminal end; the communication support module 104 is used to establish signal connection and data transmission channels between various layers of the system and various devices in the system through various communication technologies. The distribution database module 103 performs data synchronization by using a master-slave database mode, wherein the method specifically includes: judging whether the number of the main and standby data files is consistent, and if not, retransmitting the latest control file; checking the log range of the main library and comparing the change number of the control file system of the standby library, and retransmitting the latest control file if the control file of the standby library is smaller than the log range; after the control file is retransmitted, updating data file information corresponding to the control file; detecting whether the backup database data file is in the range of the main database log; retransmitting the data files with broken gears and the data files which are not sent, and updating the information of the control files; executing one-time log archiving, recording the first change number of the last record, and determining an archiving log to be transmitted; transmitting the distinct archive log from the primary repository to the backup repository; performing data recovery on the standby database to ensure that all data files are transmitted completely and the log is applied to the latest state; and re-opening the standby database, verifying whether the database can be normally opened, and checking whether the data file has transmission errors. The specific method flow for judging whether the number of the main and standby data files is consistent comprises the following steps: logging in a database, confirming a main library, and building a plurality of standby libraries by adopting a fine-grained principle; detecting and acquiring the number n of data files in a main library; detecting and acquiring the number m of data files in a database; judging whether the number n and m of the main and standby data files are consistent; if the number is inconsistent, generally n is larger than m, the latest (n-m) control files in the main library are retransmitted to the standby library; if the number is consistent, namely n is m, the subsequent steps are carried out.
The basic device includes, but is not limited to, a computer, a processor, a display terminal, a mobile terminal, etc.
The target monitoring terminal sensor includes, but is not limited to, a wind anemometer, a weather thermometer, a lightning monitoring device, a lightning current on-line monitoring instrument, a weather hydrological monitor, a rainfall monitor, and the like.
The communication technology includes, but is not limited to, wired network, local area network, wireless transmission, data traffic, bluetooth, ZigBee, and the like.
In this embodiment, a signal output end of the acquisition and transmission module 201 is connected to a signal input end of the density clustering module 202, a signal output end of the density clustering module 202 is connected to a signal input end of the quadratic clustering module 203, and a signal output end of the quadratic clustering module 203 is connected to a signal input end of the classification and statistics module 204; the acquisition and transmission module 201 is used for acquiring corresponding state data through the distributed target monitoring terminals and transmitting the state data to the data processing unit 200 of the system in real time; the density clustering module 202 is configured to randomly extract a part of sample data from the data statistically processed by the data processing unit 200, perform density-based clustering on the sampled data, and perform fast clustering to determine the number of clusters and the initial cluster center; the secondary clustering module 203 is configured to perform fast clustering on all data statistically processed by the data processing unit 200 by using the cluster number and the initial cluster center obtained by sample density clustering as input conditions; the classification and statistics module 204 is configured to classify, count, and store the detected data according to the target type of the detected data, so as to perform collision analysis on the data with correlation in the following.
Specifically, in the density clustering module 202, the density clustering method preferably adopts a DBSCAN algorithm (density-based noise application spatial clustering algorithm), and the specific steps of the algorithm are as follows:
step1, with each data point x i As a circle center, drawing a circle with eps as a radius, the circle being called x i An eps neighborhood of;
step2, calculating points contained in the circle, if the number of the points in the circle exceeds a density threshold MinPts, marking the circle center of the circle as a core point, also called a core object, if the number of the points in the eps neighborhood of a certain point is smaller than the density threshold, and only falls in the neighborhood of the core point, then the point is called a boundary point, and meanwhile, the points which are not the core point nor the boundary point are called noise points or outliers; according to the eps neighborhood and the density threshold MinPts, sequentially finishing the point type judgment of all data, and deleting noise points or outliers; core point x i All points in the neighborhood of eps are x i Direct density of (d) is not direct, if x j From x i Direct density, x k From x j Direct density, once, x n From x k Direct density, then the transmissibility of direct density can be deduced, x n From x i The density can be reached; if for x k Let x be i And x j All can be formed by x k The density can be reached, then x can be called i And x j Connecting the density, and connecting the density-connected points together to form a cluster;
step3, connecting two core points with the distance less than MinPts together to form a plurality of clusters; and assigning the boundary points to the nearest core point range until the final clustering result is formed.
In this embodiment, the signal output end of the hierarchical model module 301 is connected to the signal input end of the judgment calculation module 302, the signal output end of the judgment calculation module 302 is connected to the signal input end of the evaluation classification module 303, and the signal output end of the evaluation classification module 303 is connected to the signal input end of the risk assessment module 304; the hierarchical model module 301 is configured to select multiple indexes to perform normalization processing respectively according to the influence possibly caused by the disaster risk in the area, use each index as an initial value of disaster risk assessment, and establish a hierarchical model of risk assessment according to a risk assessment requirement; the judgment calculation module 302 is configured to construct a judgment matrix of each risk indicator factor, and obtain a weight value of each indicator in the comprehensive risk assessment through analysis and calculation of the matrix; the evaluation grading module 303 is configured to calculate a weight matrix of each index by combining a hierarchical structure analysis method, establish a mathematical model of a disaster risk degree evaluation index by combining risk evaluation indexes, and divide the risk degree of the disaster in the area into different grades according to the indexes according to a certain evaluation standard; the risk assessment module 304 is configured to perform statistical assessment and analysis on different types of disasters and risk degrees respectively according to a large number of environmental conditions in an area.
The hierarchical structure model can be divided into three or more layers, and here, a three-layer structure is preferred, and specifically, the hierarchical structure model can be: the first layer is a target layer of disaster risk degree in the area, the second layer is a criterion layer of disaster forming conditions, and the third layer is an index layer of comprehensive influence factors; specifically, the evaluation index includes, but is not limited to, the frequency of occurrence of disaster in a region, the density of regional network, the environmental condition of the region, the distribution density of population in the region, and the like.
Furthermore, the hazard level of disaster risk can be comprehensively and accurately evaluated through factors such as the evacuation difficulty of population in the area, the elimination or reconstruction difficulty of disaster in the area and the like.
Further, the judgment calculation module 302 includes a construction matrix module 3021, a level single ordering module 3022, a level total ordering module 3023, and a feature vector module 3024; a signal output end of the construction matrix module 3021 is connected to a signal input end of the hierarchical single ordering module 3022, a signal output end of the hierarchical single ordering module 3022 is connected to a signal input end of the hierarchical total ordering module 3023, and a signal output end of the hierarchical total ordering module 3023 is connected to a signal input end of the eigenvector module 3024; the construction matrix module 3021 is configured to, when there are many factors affecting disaster formation, establish a judgment matrix of a pairwise comparison matrix by pairwise comparison of the factors, so as to more intuitively reflect the relationship between the factors in the hierarchical structure; the hierarchical list sorting module 3022 is configured to perform consistency check on the constructed judgment matrix to check the rationality of the matrix and the weight vector derived therefrom, and obtain a weight vector of a group of elements to an element in a previous layer; the hierarchical total sorting module 3023 is configured to synthesize the weights under the single criterion from top to bottom to obtain a total sorting weight, and perform consistency check on the hierarchical total sorting from a high level to a low level; the eigenvector module 3024 is configured to calculate eigenvectors in the determination matrix as weights of the indexes in the comprehensive risk assessment.
Specifically, the construction matrix module 3021 establishes a pairwise comparison matrix to compare the factors pairwise by using an analytic hierarchy process principle, specifically:
if matrix
A＝(a ij ) n×n
Satisfies a ij ＞0，a ji ＝1/a ij ，
It is called a positive reciprocal matrix, where: i, j ═ 1, 2,. ·, n; n is the number of rows and columns of the matrix;
wherein, a ij The values of (c) can be scaled by the numbers 1-9 and their inverse to indicate the degree of importance between the two factors.
Wherein, the specific meanings of the numbers 1-9 and the reciprocal thereof as the scale are as follows: 1 means that the two factors have the same importance, 3 means that the two factors are slightly more important than the former, 5 means that the two factors are significantly more important than the former, 7 means that the two factors are strongly more important than the former, 9 means that the two factors are extremely more important than the former, 2, 4, 6, 8 respectively represent intermediate values of the above-mentioned adjacent judgments, and the reciprocal means that if the ratio of the importance of the factor i and the factor j is a ij The ratio of the importance of the factor j to the factor i is 1/a ij 。
The reason for constructing the judgment matrix is as follows: the hierarchical structure only reflects the relationship among all factors, the proportion of all the criteria in the single criterion layer in the target measurement is not necessarily the same, and when the factors of image disaster formation are more, the influence of all the factors on the disaster is directly considered, and the factors are probably lost due to incomplete consideration.
Specifically, in the hierarchical single-ranking module 3022 and the hierarchical total-ranking module 3023, consistency check uses a consistency ratio index C R And (4) carrying out inspection, which specifically comprises the following steps:
firstly, the consistency index C is calculated I ：
Wherein λ is max Is the maximum eigenvalue of the matrix, and n is the number of factors;
then determining corresponding average random consistency index R by table look-up I ；
Then, the consistency ratio C is calculated R And judging:
when C is present R When < 0.1, the consistency of the judgment matrix is considered to be acceptable, and when C is R And when the judgment matrix is more than 0.1, the judgment matrix is considered not to meet the consistency requirement, and the judgment matrix needs to be revised again.
After the weight vector is calculated according to the judgment matrix, the judgment matrix can be logically reasonable only through consistency check, and the result can be continuously analyzed.
Specifically, in the evaluation grading module 303, the mathematical model of the evaluation index of the disaster risk degree in the area is as follows:
wherein, w is the index of risk degree, m is the total number of evaluation indexes of risk degree, R (j) is the weighted value of each evaluation index, and Y (j) is the normalized index value of each index.
In this embodiment, the report form graphic module 401, the comprehensive report module 402, the multivariate display module 403 and the risk warning module 404 are sequentially connected through network communication; the report graph module 401 is used for drawing the data obtained by statistics, calculation and analysis into a corresponding table or a corresponding statistical graph in real time through drawing software loaded in the early warning management system; the comprehensive report module 402 is configured to generate a comprehensive disaster risk assessment early warning report by combining the data information and the drawn graph; the multivariate display module 403 is used for displaying the disaster risk conditions of the computational analysis on a display terminal in real time through a plurality of structural modes or interface forms; the risk warning module 404 is configured to issue a warning in various ways when it is monitored that certain meteorological factors reach a certain disaster risk level.
The risk warning mode comprises a flashing screen, a popup window, a warning sound, a short message notification and the like.
The embodiment also provides an operation method of the disaster risk early warning management system based on cloud computing, which comprises the following steps:
firstly, various target monitoring terminals arranged at each position in a monitoring area are used for collecting corresponding target state data in real time and transmitting the data to the data processing unit 200, performing density clustering and rapid secondary clustering operation on a large amount of data, classifying, counting and storing the data according to different monitoring targets, secondly, target data belonging to the same category are imported into a hierarchical model, the influence degree of each index factor on disaster risk is judged and calculated through a hierarchical structure analysis method, the evaluation weight value of each index factor is calculated according to the influence degree, and respectively carry out evaluation, sorting and grading operation on the disaster risk degree caused by each monitoring target, and then, drawing the real-time calculation and analysis result into an intuitive chart, automatically generating a corresponding comprehensive report for reporting, storing and managing, and finally feeding back the disaster risk predicted by analysis and the related information to public users according to a preset alarm mode.
As shown in fig. 1, the present embodiment further provides an exemplary product architecture of a disaster risk early warning management system based on cloud computing, including a computer processor 1, a computing model 2 is loaded in the computer processor 1, a plurality of target monitoring sensors 3 are connected to the outside of the computer processor 1 in a communication manner, the computer processor 1 is further connected to a cloud database 4 in a wireless manner, a display large screen 5 is connected to the outside of the computer processor 1 in a signal manner, and a user 7 can access the system and obtain feedback information through the display large screen 5 or a mobile terminal 6.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, where the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. The utility model provides a calamity risk early warning management system based on cloud calculates which characterized in that: comprises that
The system comprises a capital construction management unit (100), a data processing unit (200), a risk research unit (300) and an early warning management unit (400); the capital construction management unit (100), the data processing unit (200), the risk research unit (300) and the early warning management unit (400) are sequentially in communication connection through a network; the infrastructure management unit (100) is used for managing and controlling infrastructure operated by the early warning management system, and the infrastructure comprises equipment, a sensing device, a database and a communication technology; the data processing unit (200) is used for clustering and counting the monitoring data acquired by each target monitoring terminal; the risk research unit (300) is used for constructing a hierarchical model of disaster risks and carrying out calculation evaluation on the risk level of the disaster based on cloud calculation; the early warning management unit (400) is used for displaying the analysis results in a multivariate mode and carrying out early warning management on disaster risks;
the capital construction management unit (100) comprises a basic equipment module (101), a monitoring and sensing module (102), a distribution database module (103) and a communication support module (104);
the data processing unit (200) comprises an acquisition and transmission module (201), a density clustering module (202), a secondary clustering module (203) and a classification and statistics module (204);
the risk research unit (300) comprises a hierarchical model module (301), a judgment calculation module (302), an evaluation grading module (303) and a risk assessment module (304);
the early warning management unit (400) comprises a report form graphic module (401), a comprehensive report module (402), a multivariate display module (403) and a risk warning module (404);
the basic equipment module (101), the monitoring sensing module (102), the distribution database module (103) and the communication support module (104) are sequentially connected through network communication; the basic equipment module (101) is used for providing and managing processing equipment forming the operation of the system; the monitoring sensing module (102) is used for distributing a plurality of meteorological environment target monitoring terminal sensors which may form natural disasters in a monitoring area and acquiring corresponding state data acquired by each sensor in real time; the distributed database module (103) is used for establishing a distributed database at the cloud end based on a block chain technology so as to provide data services at the near end of the terminal; the communication support module (104) is used for establishing signal connection and data transmission channels among various layers of the system and various devices in the system through various communication technologies; the distribution database module (103) adopts a main-standby database mode to perform data synchronization, wherein the method specifically comprises the following steps: judging whether the number of the main and standby data files is consistent, and if not, retransmitting the latest control file; checking the log range of the main library and comparing the change number of the control file system of the standby library, and retransmitting the latest control file if the control file of the standby library is smaller than the log range; after the control file is retransmitted, updating data file information corresponding to the control file; detecting whether the backup database data file is in the range of the main database log; retransmitting the data files with broken gears and the data files which are not sent, and updating the information of the control files; executing one-time log archiving, recording the first change number of the last record, and determining an archiving log to be transmitted; transmitting the archive log of the differences from the primary repository to the backup repository; performing data recovery on the standby database to ensure that all data files are transmitted completely and the log is applied to the latest state; reopening the standby database, verifying whether the database can be normally opened, and checking whether the data file has transmission errors; the specific method flow for judging whether the number of the main and standby data files is consistent comprises the following steps: logging in a database, confirming a main library, and building a plurality of standby libraries by adopting a fine-grained principle; detecting and acquiring the number n of data files in a main library; detecting and acquiring the number m of data files in a database; judging whether the number n and m of the main and standby data files are consistent; if the number is inconsistent and n is larger than m, retransmitting the latest (n-m) control files in the main library to the standby library; if the number is consistent, namely n is m, entering the subsequent step;
the signal output end of the acquisition and transmission module (201) is connected with the signal input end of the density clustering module (202), the signal output end of the density clustering module (202) is connected with the signal input end of the quadratic clustering module (203), and the signal output end of the quadratic clustering module (203) is connected with the signal input end of the classification and statistics module (204); the acquisition and transmission module (201) is used for acquiring corresponding state data through the distributed target monitoring terminals and transmitting the state data to the data processing unit (200) of the early warning management system in real time; the density clustering module (202) is used for randomly extracting partial sample data from the data statistically processed by the data processing unit (200), carrying out density-based clustering processing on the sampled data, and rapidly clustering to determine the cluster number and the initial cluster center; the secondary clustering module (203) is used for performing fast clustering processing on the data statistically processed by the data processing unit (200) by taking the cluster number and the initial cluster center obtained by sample density clustering as input conditions; the classification statistical module (204) is used for classifying, counting and storing according to the target type of the detection data according to the result of the rapid clustering so as to perform collision analysis on the data with correlation in the following process;
the signal output end of the hierarchical model module (301) is connected with the signal input end of the judgment calculation module (302), the signal output end of the judgment calculation module (302) is connected with the signal input end of the evaluation grading module (303), and the signal output end of the evaluation grading module (303) is connected with the signal input end of the risk assessment module (304); the hierarchical model module (301) is used for selecting a plurality of indexes to carry out normalization processing respectively according to the influence possibly caused by disaster risks in an area, taking each index as an initial value of disaster risk assessment, and establishing a hierarchical structure model of risk assessment according to risk assessment requirements; the judgment calculation module (302) is used for constructing a judgment matrix of each risk index factor and acquiring the weight value of each index in the comprehensive risk degree evaluation through the analysis and calculation of the matrix; the evaluation grading module (303) is used for calculating a weight matrix of each index by combining a hierarchical structure analysis method, establishing a mathematical model of a disaster risk degree evaluation index by combining risk evaluation indexes, and dividing the risk degree of the disaster in the area into different grades according to the indexes according to a certain evaluation standard; the risk assessment module (304) is used for carrying out statistical assessment and analysis on different types of disasters and risk degrees respectively by combining a large number of environmental conditions in an area; the judgment calculation module (302) comprises a construction matrix module (3021), a level single ordering module (3022), a level total ordering module (3023) and a feature vector module (3024); a signal output terminal of the construction matrix module (3021) is connected to a signal input terminal of the hierarchical single ordering module (3022), a signal output terminal of the hierarchical single ordering module (3022) is connected to a signal input terminal of the hierarchical total ordering module (3023), and a signal output terminal of the hierarchical total ordering module (3023) is connected to a signal input terminal of the eigenvector module (3024); the construction matrix module (3021) is configured to, when there are many factors affecting disaster formation, establish a judgment matrix of a pairwise comparison matrix by pairwise comparison of the factors so as to more intuitively reflect the relationship between the factors in the hierarchical structure; the level single ordering module (3022) is used for carrying out consistency check on the constructed judgment matrix so as to check the rationality of the matrix and the weight vector derived from the matrix, and obtaining the weight vector of a group of elements to a certain element in the upper layer; the hierarchical total sorting module (3023) is used for synthesizing the weights under the single criterion from top to bottom to obtain a total sorting weight, and performing consistency check on the hierarchical total sorting from a high layer to a low layer; the feature vector module (3024) is configured to calculate a feature vector in the determination matrix as a weight of each index in the comprehensive risk assessment, and the hierarchical structure model is divided into three layers, specifically: the first layer is a target layer of disaster risk degree in the area, the second layer is a criterion layer of disaster forming conditions, and the third layer is an index layer of comprehensive influence factors.
2. The cloud computing-based disaster risk early warning management system according to claim 1, wherein: in the density clustering module (202), a DBSCAN algorithm is adopted as a density clustering method, and the specific steps of the algorithm are as follows:
step1, with each data point x i At the center of the circle, eps is a radius describing a circle, which is called x i An eps neighborhood of;
step2, calculating points contained in the circle, if the number of the points in the circle exceeds a density threshold MinPts, marking the circle center of the circle as a core point, also called as a core object, if the number of the points in the eps neighborhood of a certain point is smaller than the density threshold, and the point falls in the neighborhood of the core point independently, then the point is called as a boundary point, and meanwhile, the points which are not the core point nor the boundary point are called as noise points or outliers; according to the eps neighborhood and the density threshold MinPts, sequentially finishing the point type judgment of all data, and deleting noise points or outliers; core point x i All points in the neighborhood of eps are x i Direct density of (d) is not direct, if x j From x i Direct density, x k From x j Direct density, once, x n From x k Direct density, then the transmissibility of direct density can be deduced, x n From x i The density can be reached; if for x k Let x be i And x j All can be formed by x k The density can be reached, then x can be called i And x j Connecting the density, and connecting the density-connected points together to form a cluster;
step3, connecting two core points with the distance less than MinPts together to form a plurality of clusters; and assigning the boundary points to the core point range closest to the boundary points until a final clustering result is formed.
3. The cloud computing-based disaster risk early warning management system according to claim 1, wherein: in the level single ordering module (3022) and the level total ordering module (3023), consistency check adopts a consistency ratio index C R And (3) carrying out inspection, which specifically comprises the following steps:
first, the consistency index C is calculated I ：
Wherein λ is max Is the maximum eigenvalue of the matrix, and n is the number of factors;
then determining corresponding average random consistency index R by table look-up I ；
Then, the consistency ratio C is calculated R And judging:
when C is present R When < 0.1, the consistency of the judgment matrix is considered to be acceptable, and when C is R And when the judgment matrix is more than 0.1, the judgment matrix is considered not to meet the consistency requirement, and the judgment matrix needs to be revised again.
4. The cloud computing-based disaster risk early warning management system according to claim 1, wherein: in the evaluation grading module (303), the mathematical model of the evaluation index of the disaster risk degree in the area is as follows:
wherein, w is the index of risk degree, m is the total number of evaluation indexes of risk degree, R (j) is the weighted value of each evaluation index, and Y (j) is the normalized index value of each index.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111336182.6A CN113780902B (en) | 2021-11-12 | 2021-11-12 | Disaster risk early warning management system based on cloud computing |
| CN202211093017.7A CN115759729A (en) | 2021-11-12 | 2021-11-12 | Disaster risk early warning management system based on algorithm and prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111336182.6A CN113780902B (en) | 2021-11-12 | 2021-11-12 | Disaster risk early warning management system based on cloud computing |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211093017.7A Division CN115759729A (en) | 2021-11-12 | 2021-11-12 | Disaster risk early warning management system based on algorithm and prediction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113780902A CN113780902A (en) | 2021-12-10 |
| CN113780902B true CN113780902B (en) | 2022-08-05 |
Family
ID=78956987
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211093017.7A Pending CN115759729A (en) | 2021-11-12 | 2021-11-12 | Disaster risk early warning management system based on algorithm and prediction method |
| CN202111336182.6A Active CN113780902B (en) | 2021-11-12 | 2021-11-12 | Disaster risk early warning management system based on cloud computing |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211093017.7A Pending CN115759729A (en) | 2021-11-12 | 2021-11-12 | Disaster risk early warning management system based on algorithm and prediction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN115759729A (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113962992A (en) * | 2021-12-21 | 2022-01-21 | 青岛大学附属医院 | Urinary calculus flat scanning CT image recognition system based on deep learning and training method |
| CN114626475B (en) * | 2022-03-21 | 2022-11-11 | 中山大学 | Method, device and equipment for analyzing space-time change of severe drought and flood events |
| CN115270266B (en) * | 2022-08-03 | 2023-06-30 | 南昌大学 | Slope monitoring state risk judging method based on improved analytic hierarchy process |
| CN115640496B (en) * | 2022-12-16 | 2023-03-10 | 天津众联智能科技有限责任公司 | Electrical hidden danger judgment method based on improved analytic hierarchy process |
| CN116755350A (en) * | 2023-08-24 | 2023-09-15 | 深圳小米房产网络科技有限公司 | House safety monitoring and early warning system based on intelligent household internet of things technology |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106802895A (en) * | 2015-11-26 | 2017-06-06 | 阿里巴巴集团控股有限公司 | For repair data storehouse for database data method and apparatus |
| WO2020009643A1 (en) * | 2018-07-02 | 2020-01-09 | Telefonaktiebolaget Lm Ericsson (Publ) | Packet data convergence protocol data recovery |
| CN112307045A (en) * | 2020-11-11 | 2021-02-02 | 支付宝(杭州)信息技术有限公司 | Data synchronization method and system |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102945195B (en) * | 2012-11-26 | 2015-11-18 | 国电南瑞科技股份有限公司 | A kind of Master-standby redundancy replication method based on SQLite database |
| CN104252481B (en) * | 2013-06-27 | 2018-10-19 | 阿里巴巴集团控股有限公司 | The dynamic check method and apparatus of master-slave database consistency |
| CN105069160A (en) * | 2015-08-26 | 2015-11-18 | 国家电网公司 | Autonomous controllable database based high-availability method and architecture |
| CN111694894A (en) * | 2020-04-26 | 2020-09-22 | 中国建设银行股份有限公司 | Method, server, device and storage medium for monitoring data synchronization |
| CN112990797A (en) * | 2021-05-13 | 2021-06-18 | 江西省自然资源厅国土资源勘测规划院 | Disaster risk early warning management method and device based on cloud computing technology |
-
2021
- 2021-11-12 CN CN202211093017.7A patent/CN115759729A/en active Pending
- 2021-11-12 CN CN202111336182.6A patent/CN113780902B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106802895A (en) * | 2015-11-26 | 2017-06-06 | 阿里巴巴集团控股有限公司 | For repair data storehouse for database data method and apparatus |
| WO2020009643A1 (en) * | 2018-07-02 | 2020-01-09 | Telefonaktiebolaget Lm Ericsson (Publ) | Packet data convergence protocol data recovery |
| CN112307045A (en) * | 2020-11-11 | 2021-02-02 | 支付宝(杭州)信息技术有限公司 | Data synchronization method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115759729A (en) | 2023-03-07 |
| CN113780902A (en) | 2021-12-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113780902B (en) | Disaster risk early warning management system based on cloud computing | |
| CN112819207B (en) | Geological disaster space prediction method, system and storage medium based on similarity measurement | |
| US8924332B2 (en) | Forecasting hotspots using predictive visual analytics approach | |
| CN108597617A (en) | Epidemic disease grade predicting method and device, computer installation and readable storage medium storing program for executing | |
| CN104636449A (en) | Distributed type big data system risk recognition method based on LSA-GCC | |
| CN106779215B (en) | Machine learning-based power grid global delay situation sensing method | |
| CN110059668A (en) | Behavior prediction processing method, device and electronic equipment | |
| CN107679734A (en) | It is a kind of to be used for the method and system without label data classification prediction | |
| CN113781767A (en) | Traffic data fusion method and system based on multi-source perception | |
| CN110490486B (en) | Enterprise big data management system | |
| CN111223567A (en) | Suspected disease risk range calculation method based on regional medical image | |
| JP2019075017A (en) | Information processing device, risk prediction method, and program | |
| CN112101759A (en) | Method and device for constructing risk assessment model and assessing risk of expressway tunnel | |
| CN111523570A (en) | Smart city system based on community post house and control method thereof | |
| CN115470308A (en) | Method and system for constructing meteorological information management platform based on GIS (geographic information System) | |
| CN113408816B (en) | Power grid disaster situation assessment method based on deep neural network | |
| CN114880954A (en) | Landslide sensitivity evaluation method based on machine learning | |
| CN205449948U (en) | Visual system of data that gaseous benzene series physical prospecting was surveyed | |
| CN115294048A (en) | Foreign matter detection method, device, equipment and storage medium | |
| CN111654853B (en) | Data analysis method based on user information | |
| CN114417698A (en) | Rail transit external environment risk monitoring system and assessment method | |
| CN113807587A (en) | Integral early warning method and system based on multi-ladder-core deep neural network model | |
| CN107506824B (en) | Method and device for detecting bad observation data of power distribution network | |
| CN116029578B (en) | Service object business level detection method, device, electronic equipment and storage medium | |
| CN116050120B (en) | Landslide hidden danger activity remote sensing evaluation modeling method, system and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |