CN111966746B - Meteorological disaster prevention and reduction process monitoring system and monitoring method thereof - Google Patents
Meteorological disaster prevention and reduction process monitoring system and monitoring method thereof Download PDFInfo
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Abstract
The invention relates to a method for monitoring a meteorological disaster prevention and reduction process realized by a computer, which comprises the following steps: lane receiving step: receiving weather data using at least one weather station data live lane, and receiving event data using at least one event data lane; lane data processing step: analyzing the time, the event position and the event type of the event data, and analyzing the time and the data magnitude in the weather data based on weather stations around the event position; lane interface display step: generating an event thumbnail according to the time and the event type of the event data is displayed on one discrete lane unit of the discrete lanes, and generating a curve or a bar graph according to the time and the data magnitude of the meteorological data is displayed on the meteorological station data live lane. The monitoring system flow and the algorithm have better accuracy and timeliness for improving the disaster prevention and reduction capability of the weather, and provide better support in the aspects of improving the disaster prevention and response capability.
Description
Technical Field
The invention belongs to the field of execution design of user interfaces for electric digital processing, and particularly relates to a weather disaster prevention and reduction flow monitoring system and a monitoring method thereof.
Background
On the basis of a national early warning release system, each province actively promotes the informatization construction of early warning in the province, builds a provincial early warning release system, and realizes on-demand application and on-demand supply. The basic weather disaster prevention and reduction service processes of each province are independent, intermediate media for vertical management and outstanding service effects are absent, a clear unified whole process monitoring method is absent, effective association cannot be carried out on live conditions, early warning, service and disaster public opinion, and early warning release effects and meteorological guarantee service advantages are not reflected. Therefore, in order to comprehensively master the actual conditions of the weather disasters in various stages from occurrence, development to ending, early warning, service and disaster public opinion, establishing the whole flow monitoring of weather disaster prevention and reduction has important significance for expanding the service functions of the weather disaster prevention and reduction technology, improving the disaster prevention and response capability and realizing transparent management.
Disclosure of Invention
In order to solve the technical problem, the invention provides a method for monitoring a weather disaster prevention and reduction process implemented by a computer, which is based on at least a first user interface, wherein the first user interface is a map monitoring interface, the method further comprises monitoring based on a second user interface, and the method for monitoring by the second user interface comprises the following steps:
lane receiving step: receiving meteorological data by at least one meteorological station data live lane, and receiving event data by at least one event data lane, wherein the meteorological data comprises temperature data, precipitation data and wind direction data, and the event data comprises disaster public opinion event data, service event data and early warning event data;
lane data processing step: analyzing the time, the event position and the event type of the event data, and analyzing the time and the data magnitude in the weather data based on weather stations around the event position;
lane interface display step: generating an event thumbnail according to the time and the event type of the event data, displaying the event thumbnail on one discrete lane unit of the discrete lanes, and generating a curve or a bar graph according to the time and the data magnitude of the meteorological data, displaying the curve or the bar graph on the live lane of the meteorological station data, wherein the time of the meteorological data is the abscissa, and the data magnitude is the ordinate.
In another aspect, the invention further provides a computer-implemented weather disaster prevention and reduction process monitoring system, which comprises at least one data processor; and a memory storing instructions that, when executed by the at least one processor, implement the methods provided by the present invention.
The invention has the beneficial effects that by constructing a whole-flow monitoring system of the weather disaster prevention and reduction service, establishing national weather disaster prevention and reduction data standard specifications and shared transmission channels, designing and realizing each service flow monitoring algorithm, carrying out horizontal and longitudinal visual display from two dimensions of time space by means of a lane diagram, dynamically grasping basic weather disaster prevention and reduction work information and key nodes, avoiding the respective war of basic weather disaster prevention and reduction, forming a weather disaster prevention and reduction ecological chain, realizing the whole-flow whole-element visual dynamic monitoring and making up the blank of the national weather disaster prevention and reduction service system. Through practice, the monitoring system flow and algorithm have better accuracy and timeliness for improving the disaster prevention and reduction capability of weather, and better support is provided for improving the disaster prevention and response capability.
Drawings
FIG. 1 is a schematic diagram of a first user interface and a second user interface;
FIG. 2 is a schematic diagram of a third user interface and a second user interface;
FIG. 3 is a diagram showing the whole flow of the weather disaster prevention and reduction.
Detailed Description
In some embodiments of the present invention, a method for monitoring a weather disaster prevention and reduction process implemented by a computer is shown in fig. 1, where the monitoring is based on at least a first user interface, the first user interface is a map monitoring interface, the method further includes monitoring based on a second user interface, and the method for monitoring by the second user interface includes:
lane receiving step: receiving meteorological data by at least one meteorological station data live lane, and receiving event data by at least one event data lane, wherein the meteorological data comprises temperature data, precipitation data and wind direction data, and the event data comprises disaster public opinion event data, service event data and early warning event data;
lane data processing step: analyzing the time, the event position and the event type of the event data, and analyzing the time and the data magnitude in the weather data based on weather stations around the event position;
lane interface display step: generating an event thumbnail according to the time and the event type of the event data, displaying the event thumbnail on one discrete lane unit of the discrete lanes, and generating a curve or a bar graph according to the time and the data magnitude of the meteorological data, displaying the curve or the bar graph on the live lane of the meteorological station data, wherein the time of the meteorological data is the abscissa, and the data magnitude is the ordinate.
In some embodiments, the meteorological data lanes or the event data lanes are located in different blocks, respectively.
In some embodiments, the time granularity of the discrete lanes includes scaling support for day, hour, ten minute, minute periods.
In some embodiments, the length of a plurality of said time thumbnails located in the same said discrete lane unit is inversely proportional to the number of thumbnails in that discrete lane unit.
In some embodiments, the time difference between two adjacent event data is smaller than the lane time period, and when the two event data belong to the same event, the thumbnails corresponding to the two discrete data are merged along the lane direction.
In some embodiments, the event data further includes a rating attribute associated with a background color of the surgical thumbnail; the background color of the thumbnail is attached with text information and disaster type icons.
In some embodiments, the lane-extension direction scales steplessly.
In some specific embodiments, the data magnitude is a level value obtained by a grading algorithm of CIMISS data, the intersection relation between the weather station and the current early warning landing zone is calculated based on a geographic information geometric analysis algorithm through landing zone information of early warning signals and longitude and latitude of the weather station, and the weather station magnitude value around the current early warning event is obtained through statistics. For example: the levels include red warning, orange warning, yellow warning, blue warning.
In some embodiments, in the lane processing step, the processing method of the early warning event data is as follows: creating an early warning index library based on the low language according to the national and provincial weather disaster early warning signal release regulations, carrying out statistical analysis on basic data according to index elements and time ranges of the early warning index library, acquiring elements and time dimensions related to early warning indexes, calculating average values or statistical values of the elements in a past period, accumulating or averaging missing hour-by-hour data needing secondary statistics, comparing current hour data with the early warning indexes, and recording weather site information meeting the early warning release standard; after the basic data analysis is completed, a message is sent to a downstream index comparison program through a message middleware, and the index comparison program stores site information and early warning information which accord with the early warning release standard into a database through fast comparison of indexes and analysis data. And when one site accords with a plurality of early warning level indexes, only the highest early warning level of the site is recorded. For example: the 54511 station accords with the orange early warning, the yellow early warning and the blue early warning of the high-temperature early warning, and only the 54511 station is stored as the orange early warning.
In some embodiments, the event data lanes include multiple levels of early warning sub-axes, and the lane interface displaying step includes an early warning event category step:
defining an early warning event;
highlighting the selected content according to weather early warning information;
based on the time axis and the multi-stage early warning sub-axis, the area to which the early warning belongs is taken as the center through the association condition,
administrative division codes are extracted through the early warning unique identification, and the city and county coding rules are coded according to the administrative division codes, for example: the only representation of a certain pre-alarm is: 6301234160000_20200603161545, corresponding administrative division code is 630123, according to administrative division code coding rule, the municipal grade administrative division corresponding to the county grade administrative division is 630100, the provincial grade administrative division is 630000, upper and lower grade related early warning information is screened out and displayed in a grading manner, and the related conditions comprise a release unit, an early warning type, an early warning grade and release time.
In some embodiments, the different color regions of the bar graph correspond to respective level values, and the different color region heights are proportional to respective levels of weather station count values.
In still other embodiments of the present invention, the method for monitoring the first user interface includes:
map receiving step: receiving early warning situation data and emergency data by using a three-dimensional map;
map data processing step: acquiring the geographic position and the topography of the current event and the weather station information around the current event;
map interface display step: the detailed information, geographic location and topography of the current event, and the locations of weather stations surrounding the current event are displayed in a three-dimensional map.
In other specific embodiments, the map layer of the three-dimensional map comprises an emergency, an early warning situation, basic information, hidden points, a map layer and an area name, and the basic information comprises disaster responsible persons, informations, early warning equipment, schools, hospitals, tourist attractions, inflammable and explosive places and mountain reservoirs.
In still other embodiments of the present invention, as shown in connection with fig. 2, the monitoring is further performed based on a third user interface, and the method for performing monitoring by using the third user interface includes:
interface receiving step: receiving national warning big horn data, national weather display screen data, information person data and weather disaster prevention and reduction 'one account' data by utilizing at least one block in an interface, wherein the weather disaster prevention and reduction 'one account' data is provincial uploading data;
and a data processing step: obtaining updated data according to the time attribute of the data;
interface display step: the update data is displayed in at least one block.
In some preferred embodiments of the present invention, the first user interface and the second user interface perform association monitoring, and the associating step includes:
the map receiving step and the lane receiving step are synchronized to receive data;
a lane data processing step of processing the processed data in synchronization with the map data processing step;
the lane interface displaying step is displayed simultaneously with or separately from the map interface displaying step.
The following examples continue to illustrate the invention but should not be construed to limit the scope of the invention.
The invention aims to provide a full-flow monitoring method aiming at a disaster process, which is used for comparing live, early warning, service and disaster public opinion from two dimensions of time and space based on a lane diagram, is convenient for embodying weather disaster prevention and reduction advantages, presents service effects, and provides insight and problems in advance so as to avoid timely and make a reasonable decision.
Based on the above purpose, the embodiment of the invention provides a brand new full-flow monitoring method for weather disaster prevention and reduction, which is mainly aimed at: typhoons, heavy rain, snow storms, cold tides, strong winds, sand storms, high temperatures, drought, thunder, hail, frost, heavy fog, haze, road icing and other disastrous weather with great influence and strong destructiveness and serious and extra-serious emergencies. The weather disaster prevention and reduction full-flow monitoring method comprises the following steps:
1. meteorological disaster prevention and reduction process monitoring
The weather disaster prevention and reduction is a primary link and a first path of disaster prevention and reduction line of the national disaster prevention and reduction, and is divided into four axes of live, early warning, service and disaster public opinion longitudinally by taking a 'lane diagram' as a time axis and aiming at the full-flow monitoring of serious and oversized emergencies and early warning information caused by the weather disaster. And the data is in compliance with national standard specification of the data format of the weather disaster prevention and reduction, and the data of the basic weather disaster prevention and reduction business is cleaned and tidied through a data transmission sharing channel of the weather disaster prevention and reduction in national province. The following cleaning order is followed for each shared data file: the first step: and (5) pretreatment. Performing normalization check, and filtering files and records which are out of normalization and cannot be further processed; and a second step of: and (5) data cleaning. According to the cleaning rules described below, dirty data (incomplete, error, etc.), abnormal data (repeated, overtime, inconsistent, etc.) records are automatically detected and filtered, and automatic complementation of missing spatial information attributes (longitude and latitude, administrative codes, addresses, etc.) is attempted; and a third step of: quality scoring. Automatically generating a quality score based on the previously discovered problems; fourth step: correcting the update. The report unit is notified of the problem found by the inspection with reference to the score, and a correction or update procedure is triggered. The following cleaning rules are implemented according to the data quality management "six elements":
1. data integrity. Identify and resolve incomplete or missing records and attributes. Record integrity is by counting the spatial distribution and type distribution of shared data, detecting missing records and missing types. The attribute integrity is whether the null value is checked according to the selection of the field in the national weather disaster prevention and reduction data format standard, and the null value judgment adopts a text length and a null value word list;
2. data normalization. Identifying and solving the problems of naming standards, format standards, date and time standards, telephone number standards, precision standards and the like. The naming standards check whether the file name meets the file naming rules, the format standards check whether the XML format is met, the key fields exceed the specification of the dictionary table, the date and time standards check whether the date and time format is met, the telephone number standards check whether the telephone number is long enough, the mobile phone number is compliant, and the accuracy standards check whether the longitude and latitude and administrative codes are fine enough. The format specification check mainly adopts a regular expression method, and attempts to correct key fields which do not accord with a dictionary table by adopting a word vector clustering mode;
3. data consistency. And identifying and solving the consistent problems of space, early warning business, responsible person, reporting source and the like. The space consistency check is used for checking whether longitude and latitude, administrative codes and addresses are matched and whether the same ground source is pointed, the early warning business consistency check is used for checking whether the early warning issuing facility, the issuing facility state and the equipment codes in issuing feedback are matched, the responsibility person consistency check is used for checking whether the issuing mobile phone, the responsibility person, the monitoring point responsible person and the mobile phone of the responsibility person are matched, and the reporting source consistency check is used for checking whether the code of the filling unit is consistent with the filling data unit. Consistency check is mainly realized through database indexes and external keys;
4. accuracy of data. And identifying and solving the error problems of early warning, precipitation threshold, administrative region coding, place name and the like. And checking whether the early warning ID accords with the specification and issuing feedback is an early warning in effect according to the early warning business rule, wherein the rule adopts a regular expression, and the effect check adopts a retrieval mode. And counting the maximum precipitation amount (water level and soil) of each city and county for about 10 years day/hour by adopting an abnormality detection mode based on statistics, and checking the extremely poor distance and the quartile distance of the precipitation (water level and soil) threshold value to detect the threshold value abnormal point. Making a code dictionary and a place name dictionary rule of a national administrative district in the last 30 years, and identifying wrong administrative codes and place names;
5. data uniqueness. Identifying and solving the repeated recording problem, and judging whether the main key identification data is repeated with the stored record according to the record;
6. data timeliness. And identifying and solving the real-time data aging problem, adopting distance-based anomaly detection, formulating a timeout threshold according to transmission specifications, and checking whether the data is overtime or abnormally interrupted.
The national weather disaster prevention and reduction data are effectively standardized through the cleaning sequence and the cleaning rule, the data quality is improved, the high availability of the data is ensured, the data are enabled to be 'live' by using the visualization technology, important links and key works of weather disaster prevention and reduction are better mastered, global deployment and decision support are well made, and the emergency 'war' state is reasonably entered according to related emergency response commands. And collecting, correlating and displaying the weather disaster Internet big data by using the crawler technology, the machine learning technology, the natural language processing technology and the like.
(1) Emergency event
The live shaft selects the nearest meteorological observation station (automatic station) in the affiliated range according to the emergency place, and gives out air temperature, precipitation and wind power wind direction; the early warning shaft is divided into four sub-shafts of national province and city county, and the county early warning related to the type and the place of the emergency is displayed by taking the emergency as a main line, and the county early warning and the national early warning which belong to the city level, the province level and the national level upwards are displayed; the service axis is divided into two levels of sub-axes of China and city and county, and comprises call response information (telephone outbound), decision service information (two-office information, express mail and special report), consultation video connection, emergency starting command, important wholesale, important command and the like; the disaster public opinion axis is matched with the emergency, wherein the disaster comprises disaster occurrence initial conditions, disaster receiving conditions, derived and secondary disasters, disaster receiving specific conditions, loss, rescue conditions and the like. Public opinion includes social and public trends of events, warning of the reverberations of publications and other related content.
Combining a 'lane diagram' time line, adopting 'electrodeless scaling' to perform early warning and comparing live conditions with disaster conditions, and giving out timeliness of early warning release; the service is compared with the early warning, and important links, important nodes and behavior actions in the early warning effective period are highlighted; the disaster public opinion is compared with the service to reflect the forward and reverse information of the public to the action of the government department in the emergency; through live, early warning, service, disaster public opinion overview event lines, reasonable control and reasonable defense arrangement are achieved.
(2) Early warning event
According to the city and county issuing units of early warning issuing, the real-time shaft is combined with weather hazard early warning signal issuing standards and defense guidelines of each province to count weather observation stations (automatic stations) reaching the red, orange and blue standards in the scope of the province, all the real-time stations reaching the red, orange and blue standards in the province are presented in a province-level early warning mode in a histogram mode through four colors of the red, orange and blue, all the real-time stations reaching the red, orange and blue standards in the city are presented in a city-level early warning mode, and all the real-time stations reaching the red, orange and blue standards in the county-level early warning mode are presented in the county. Clicking a single site to enter a specific site to present the current relevant meteorological element value; the early warning shaft takes provincial early warning as a main line, and displays provincial early warning, all early warning of downtown county and upward national early warning. And displaying the city level early warning, all county level early warning and the provincial level early warning. The county level early warning is taken as a main line, and the county level early warning and the provincial level early warning and the national level early warning which belong to the county level upwards are displayed; the service axis is divided into two levels of sub-axes of China and city and county, and comprises call response information (telephone outbound), decision service information (two-office information, express mail and special report), consultation video connection, emergency starting command, important wholesale, important command and the like; the disaster public opinion axis is matched with the early warning type, wherein the disaster comprises disaster occurrence initial conditions, disaster receiving conditions, derived and secondary disasters, disaster receiving specific conditions, loss, rescue conditions and the like. Public opinion includes social and public trends of events, warning of the reverberations of publications, and other content.
Combining a 'lane diagram' time line, adopting 'electrodeless scaling', comparing early warning with a live situation, combining an early warning release standard, counting live situation information reaching an early warning release level in an hour, and giving out relevant meteorological element values and early warning release timeliness; compared with the early warning, the service highlights the national province, city and county level four important links, important nodes and behavior actions in the early warning effective period; disaster public opinion and service pair
The ratio reflects forward and reverse information released by the public on the early warning; the early warning release effect is controlled by the early warning release period of live, early warning, service and disaster public opinion overview based on the Meteorological disaster early warning signal quality inspection method, to perfect early warning information advance index.
By comparing the horizontal and vertical directions of the full-flow data of the weather disaster prevention and reduction, the full-flow monitoring of the weather disaster prevention and reduction from the occurrence and the development to the end of the disaster is realized for the first time, and the blank of the full-flow monitoring in the disaster prevention and reduction service system of the city and county of China is made up. The early warning is compared with the live situation and the disaster situation, and the timeliness of the early warning release can be obtained; compared with the early warning, the service can highlight important service, important links, behavior actions and response speed in the early warning effective period; the disaster public opinion is compared with the service to reflect the forward and reverse attitudes of the public to the actions of the government departments in the emergency. The process realizes the transverse and longitudinal overview of live, early warning, service and disaster public opinion, and realizes reasonable distribution and control and reasonable defense arrangement. The monitoring flow chart is shown in fig. 3.
2. Meteorological disaster prevention and reduction monitoring method
(1) Live data extraction
According to the Meteorological disaster early warning signal quality inspection method, an owl language is used for creating an early warning index library, elements and time dimensions related to early warning indexes are obtained, missing hour-by-hour data needing secondary statistics are accumulated or averaged, the current hour data is compared with the early warning indexes, and site information meeting the early warning release standard (red, orange, yellow and blue) is recorded. And the same type of early warning is performed, and when one site accords with a plurality of early warning levels, only the highest level is recorded.
(2) Early warning event analogy
And defining an early warning event according to the Meteorological disaster early warning signal quality inspection method. According to the weather early warning information, the selected content is highlighted, and based on a time axis and four-level early warning sub-axes of the city and county of the national province, the relevant early warning information of the upper and lower levels is screened out by issuing relevant conditions such as units, early warning types, early warning grades and the like by taking the area of the early warning as the center, and is displayed in a grading manner.
(3) Service data selection
The service axis is a core axis and is used for tracing important behaviors and important nodes from disaster occurrence and development to end. And (3) dynamically extracting disaster type information in service data by using a natural language processing technology, and selecting service information of disaster events and early warning events by matching disaster type rules (such as strong correlation of debris flow and heavy rain, strong convection, hail and the like), wherein response behaviors such as working dynamics, emergency response, special plans, decision materials, important wholesale and the like are respectively and dynamically displayed.
(4) Event public opinion acquisition
After the disaster occurs, disaster public opinion data related to the disaster are acquired on the Internet by utilizing a crawler technology according to the disaster type, the occurrence time and the place area, a disaster model is established by utilizing a machine learning and natural language processing technology, data extraction and aggregation analysis are carried out, and the current disaster loss, rescue conditions and the reflecting conditions of the masses to disaster events are given.
(5) Disaster process linkage
In order to realize the whole process monitoring from the occurrence and development of the meteorological disaster to the end, the live condition, the early warning, the service and the disaster public opinion are indispensable, the meteorological element value of the live condition monitoring meeting the early warning type is selected between the live condition and the early warning through the early warning type, the early warning release time is compared with the monitoring station value time reaching the early warning release standard, and the early warning release advance is intuitively reflected. The service takes the early warning as a starting point, reflects that when the national province and city county level issues higher-level early warning or serious extra-large weather disasters occur, four-level departments of the national province and city and county respectively issue an emergency response starting command, execute important government instructions, implement important central wholesale indication, take key actions and important behaviors carried out in the emergency process in the modes of video consultation, telephone calling and the like, and reflect the effect of early warning issue and whether the behaviors in the service process are effective through disaster public opinion. Through four-axis intersection comparison, the whole flow of weather disaster prevention and reduction in the disaster process is intuitively reflected, the vertical supervision capability of weather is exerted, and the comprehensive strength of weather disaster prevention and reduction is improved.
3. Meteorological disaster prevention and reduction function realization
And the displaying mode of stepless scaling of the lane diagrams is used, the live, early warning, service and disaster public opinion are displayed in a classified mode and listed item by item based on a time axis, and four-axis linkage is realized by effectively focusing on key nodes and important links of a full chain of weather disaster prevention and reduction from the occurrence and development of disasters to the end of the disasters in two dimensions of time and space.
The lane diagram electrodeless scaling function uses a container of a disaster event lane diagram, realizes grouping arrangement of various levels of early warning, service and disaster public opinion of a plurality of lanes through a custom template, and adds scaling support for time periods of day, hour, ten minutes and minutes by using custom time granularity, so that the time period distribution is more reasonable after a time axis is scaled. After data update, the data is clustered by category, for example: counting the total number of the pieces of service information at the same time point and displaying the total number in a lane diagram in a window, when clicking the window, expanding a plurality of accumulated service information lists, clicking any one of the selected lists again, and viewing specific service information to solve the phenomenon of data stacking caused by dense distribution of the encountered time points; the weather station data live lane and event data lane use the same time axis, when the time granularity of the live lane diagram changes, the event lanes can be automatically adjusted to the same time granularity to ensure the consistent data display range, and the display effect of different time granularities is adjusted to a certain degree. For example: when the mouse is scrolled in the live lane or the event data lane diagram, the time granularity displayed in the live lane and the event data lane can be automatically zoomed at the same time, and the service information displayed in the lane diagram can be displayed according to the zoom of the time granularity.
The embodiments and functional operations of the subject matter described in this specification can be implemented in the following: digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware, including the structures disclosed in this specification and structural equivalents thereof, or a combination of one or more of the foregoing.
Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on one or more tangible, non-transitory program carriers, for execution by, or to control the operation of, data processing apparatus.
In combination with the business requirements of users, the software platform matched with the planned visualization system comprises: the system comprises a visual rendering operation platform, a three-dimensional rendering plug-in, a data service platform and a map service platform, so as to support the construction of a visual system and the realization of system functions. The software architecture is as follows:
alternatively or additionally, the program instructions may be encoded on a manually-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution by data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of the foregoing.
The term "data processing apparatus" encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or multiple computers. The device may comprise a dedicated logic circuit, for example an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). The apparatus may include, in addition to hardware, code that creates an execution environment for the relevant computer program, such as code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.
A computer program (which may also be referred to or described as a program, software application, module, software module, script, or code) can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. The computer program may, but need not, correspond to a file in a file system. A program may be stored in a portion of a file that holds other programs or data, e.g., one or more scripts stored in the following: in a markup language document; in a single file dedicated to the relevant program; or in a plurality of coordinated files, for example files that store one or more modules, subroutines, or portions of code. A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
A computer suitable for carrying out the computer program comprises and can be based on a general purpose microprocessor or a special purpose microprocessor or both, or any other kind of central processing unit, as examples. Typically, the central processing unit will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a central processing unit for executing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, the computer does not have to have such a device. In addition, the computer may be embedded in another apparatus, such as a mobile phone, a Personal Digital Assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a removable storage device, such as a Universal Serial Bus (USB) flash drive, etc.
Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices including by way of example: semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks, for example, internal hard disks or removable disks; magneto-optical disk; CD-ROM and DVD-ROM discs. The processor and the memory may be supplemented by, or incorporated in, special purpose logic circuitry.
Claims (14)
1. The method for monitoring the weather disaster prevention and reduction process realized by the computer is at least based on a first user interface, wherein the first user interface is a map monitoring interface, and is characterized by further comprising the step of monitoring based on a second user interface, and the method for monitoring by the second user interface comprises the following steps:
lane receiving step: receiving weather data of a nearest weather observation station in a range selected according to an emergency place by at least utilizing one weather station data live lane, and receiving event data by at least utilizing one event data lane, wherein the weather data comprises temperature data, precipitation data and wind direction data, and the event data comprises disaster public opinion event data, service event data and early warning event data; the disaster public opinion event data comprises disaster condition data and public opinion data, wherein the disaster condition data comprises disaster occurrence starting condition data, disaster receiving condition data, derivative data, secondary disaster data, disaster receiving specific conditions, loss data and rescue condition data, and the public opinion data comprises social and public trends on events, warning issued reverberations and other related contents; the service event data comprises call response information, decision service class information, consultation video connection, emergency starting commands, important wholesale and important commands;
the processing method of the disaster public opinion event data comprises the following steps: after a disaster occurs, acquiring disaster public opinion event data related to the disaster by utilizing a crawler technology on the Internet according to the type, the occurrence time and the place area of the disaster, establishing a disaster model by utilizing machine learning and natural language processing, extracting and aggregate analyzing the data, and giving out the current disaster loss, rescue conditions and the reflecting conditions of the public on the disaster event;
lane data processing step: analyzing the time, the event position and the event type of the event data, and analyzing the time and the data magnitude in the weather data based on weather stations around the event position; the processing method of the early warning event data comprises the following steps: creating an early warning index library based on owl language, carrying out statistical analysis on basic data according to index elements and time ranges of the early warning index library, obtaining elements and time dimensions related to early warning indexes, calculating average values or statistical values of the elements in a past period of time, accumulating or averaging missing hour-by-hour data needing secondary statistics, comparing current hour data with the early warning indexes, and recording weather site information meeting early warning release standards; after the basic data analysis is completed, a message is sent to a downstream index comparison program through a message middleware, and the index comparison program stores site information and early warning information which accord with the early warning release standard into a database by rapidly comparing the index with analysis data; when one site accords with a plurality of early warning level indexes, only the highest early warning level of the site is recorded;
lane interface display step: generating an event thumbnail according to the time and the event type of the event data, displaying the event thumbnail on a discrete lane unit of a discrete lane, and generating a curve or a bar graph according to the time and the data value of the meteorological data, displaying the curve or the bar graph on the data live lane of the meteorological station, wherein the time of the meteorological data is an abscissa, and the data value is an ordinate;
the weather station data live lanes and event data lanes use the same time axis, when the time granularity of the live lane diagram changes, the event lanes can be automatically adjusted to the same time granularity to ensure the consistent data display range, and the display effect of different time granularities is adjusted to a certain degree;
the weather station data live lanes and event data lanes are scaled steplessly in the direction of elongation.
2. The method of claim 1, wherein the meteorological data lanes or the event data lanes are each located in a different block.
3. The method of claim 2, wherein the time granularity of the discrete lanes comprises scaling support for day, hour, ten minute, and minute periods.
4. The method of claim 3, wherein the length of a plurality of said event thumbnails located in the same said discrete lane unit is inversely proportional to the number of thumbnails in that discrete lane unit.
5. A method as claimed in claim 3, wherein the time difference between two adjacent event data is less than the lane time period and the thumbnails corresponding to the two discrete data are merged in the lane direction when they belong to the same event.
6. The method of claim 3, wherein the event data further comprises a rating attribute associated with a background color of the thumbnail; the background color of the thumbnail is attached with text information and disaster type icons.
7. The method of claim 3, wherein the data magnitude is a level value obtained by a grading algorithm for CIMISS data, the intersection relationship between the weather station and the current early warning landing zone is calculated based on a geographic information geometric analysis algorithm through landing zone information of early warning signals and longitude and latitude of the weather station, and the weather station magnitude value around the current early warning event is obtained through statistics; the level value comprises red early warning, orange early warning, blue early warning and yellow early warning.
8. The method of claim 1, wherein the event data lanes include multiple levels of early warning sub-axes, and the lane interface displaying step includes an early warning event category step:
defining an early warning event;
highlighting the selected content according to weather early warning information;
based on the time axis and the multi-stage early warning sub-axis, the related early warning information of the upper and lower stages is screened out by taking the area to which the early warning belongs as the center through the association condition and is displayed in a grading manner, wherein the association condition comprises a release unit, an early warning type, an early warning grade and release time.
9. The method of claim 8, wherein different color regions of the bar graph correspond to respective level values and different color region heights are proportional to respective levels of weather station count values.
10. The method of claim 9, wherein the method of monitoring the first user interface comprises:
map receiving step: receiving early warning situation data and emergency data by using a three-dimensional map;
map data processing step: acquiring the geographic position and the topography of the current event and the weather station information around the current event;
map interface display step: the detailed information, geographic location and topography of the current event, and the locations of weather stations surrounding the current event are displayed in a three-dimensional map.
11. The method of claim 10, wherein the layers of the three-dimensional map comprise emergencies, pre-warning situations, basic information, hidden points, map layers, area names, the basic information comprising disaster responsible persons, informations, pre-warning devices, schools, hospitals, tourist attractions, inflammable and explosive places, mountain reservoirs.
12. The method of claim 10, further based on a third user interface, wherein the method of monitoring the third user interface comprises:
interface receiving step: receiving national warning big horn data, national weather display screen data, information person data and weather disaster prevention and reduction 'one account' data by utilizing at least one block in an interface, wherein the weather disaster prevention and reduction 'one account' data is provincial uploading data;
and a data processing step: obtaining updated data according to the time attribute of the data;
interface display step: the update data is displayed in at least one block.
13. The method of claim 1, wherein the first user interface and the second user interface are associated for monitoring, the associating step comprising:
the map receiving step and the lane receiving step are synchronized to receive data;
a lane data processing step of processing data in synchronization with the map data processing step;
the lane interface displaying step is displayed simultaneously with or separately from the map interface displaying step.
14. A computer-implemented meteorological disaster prevention and reduction process monitoring system is characterized in that,
the system includes at least one data processor; and
memory storing instructions which, when executed by at least one processor, implement a method according to any one of claims 1-13.
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| CN113609406B (en) * | 2021-07-27 | 2024-02-20 | 杭州鸿泉物联网技术股份有限公司 | Overhead operation wind situation information sharing method, system and equipment based on geocoding |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2786303A1 (en) * | 2010-01-19 | 2011-07-28 | Swiss Reinsurance Company Ltd. | Method and system for automated location dependent natural disaster forecast |
| US8806361B1 (en) * | 2013-09-16 | 2014-08-12 | Splunk Inc. | Multi-lane time-synched visualizations of machine data events |
| CN104950351A (en) * | 2014-09-04 | 2015-09-30 | 国网山东省电力公司应急管理中心 | Meteorology-based multi-meteorological-element composite horizontal display method and system thereof |
| CN104951993A (en) * | 2014-09-04 | 2015-09-30 | 国网山东省电力公司应急管理中心 | Comprehensive monitoring and early warning system based on meteorology and power grid GIS and method thereof |
| CN104951493A (en) * | 2014-11-27 | 2015-09-30 | 国网山东省电力公司应急管理中心 | Method and system for correlating weather information with power equipment on basis of GIS (geographic information system) |
| CN107332889A (en) * | 2017-06-20 | 2017-11-07 | 湖南工学院 | A kind of high in the clouds information management control system and control method based on cloud computing |
| CN109583707A (en) * | 2018-11-08 | 2019-04-05 | 北京车和家信息技术有限公司 | Process transaction processing method and processing device, computer equipment and readable storage medium storing program for executing |
-
2020
- 2020-08-10 CN CN202010796275.6A patent/CN111966746B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2786303A1 (en) * | 2010-01-19 | 2011-07-28 | Swiss Reinsurance Company Ltd. | Method and system for automated location dependent natural disaster forecast |
| US8806361B1 (en) * | 2013-09-16 | 2014-08-12 | Splunk Inc. | Multi-lane time-synched visualizations of machine data events |
| CN104950351A (en) * | 2014-09-04 | 2015-09-30 | 国网山东省电力公司应急管理中心 | Meteorology-based multi-meteorological-element composite horizontal display method and system thereof |
| CN104951993A (en) * | 2014-09-04 | 2015-09-30 | 国网山东省电力公司应急管理中心 | Comprehensive monitoring and early warning system based on meteorology and power grid GIS and method thereof |
| CN104951493A (en) * | 2014-11-27 | 2015-09-30 | 国网山东省电力公司应急管理中心 | Method and system for correlating weather information with power equipment on basis of GIS (geographic information system) |
| CN107332889A (en) * | 2017-06-20 | 2017-11-07 | 湖南工学院 | A kind of high in the clouds information management control system and control method based on cloud computing |
| CN109583707A (en) * | 2018-11-08 | 2019-04-05 | 北京车和家信息技术有限公司 | Process transaction processing method and processing device, computer equipment and readable storage medium storing program for executing |
Non-Patent Citations (1)
| Title |
|---|
| 贵州省气象观测资料到CIMISS流程的梳理分析;汤宁;杨士进;刘崛;李波;支亚京;;福建电脑(第07期);全文 * |
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