CN116070793A - Hydrologic disaster prediction system based on data processing - Google Patents
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Abstract
The invention discloses a hydrologic disaster prediction system based on data processing, which relates to the technical field of hydrologic disaster prediction and is used for solving the problems that the existing hydrologic disaster prediction cannot make specific precautionary measures according to specific categories and specific probabilities of occurrence of hydrologic disasters, is easy to cause panic of residents and can waste social resource utilization; the invention collects the place, type and probability of the occurrence of the hydrologic disaster through the existing hydrologic forecast, and generates the early warning of different grades by utilizing the analysis module and the hydrologic disaster of different types and probability, and the prevention module is matched to make the prevention measures of different grades for different early warning, so that the utilization of social resources is more reasonable, and the panic caused by the hydrologic disaster with low probability to residents is avoided.
Description
Technical Field
The invention relates to the technical field of hydrologic disaster prediction, in particular to a hydrologic disaster prediction system based on data processing.
Background
The hydrologic disaster in China mainly comprises flood disasters and storm surge disasters, wherein the flood disasters are characterized in distribution: dongduoxil is little; coastal land is more and inland land is less; the land is much lower and the mountain land is little; the mountains have more southeast slopes and less northwest slopes. The main categories are storm flood: concentrated distribution in the season wind area, and the space-time distribution is consistent with the storm space-time distribution, and the snow melting flood is realized: distributed in northwest and northeast areas, and spatially distributed: snow melting: 4. 5 months, northeast glacier water melting: 7-8 months, northwest; slush flood (flood): upstream Ningxia, inner Mongolia river reach, downstream partial river reach and Songhua river partial river reach flood conditions: and (A) sealing and freezing: the average temperature of the coldest month is lower than 0C, and the flow direction of B is: from low weft or south to high weft or north. The storm tide is divided into typhoon storm tide and temperate storm tide according to the factors; the cause is mainly from the strong wind continuously blowing on land: such as the northeast wind in autumn and winter and typhoons in summer, strong winds cause the rapid increase of seawater: advantageous topography: the coastline or the bay is in a horn shape, and the beach is gentle; the tide is enhanced when the astronomical tide is generated.
The hydrologic forecasting refers to making qualitative or quantitative forecasting on hydrologic conditions of a certain water body, a certain region or a certain hydrologic station in a certain time in the future according to the prior or current hydrologic meteorological data, and provides basis for flood prevention and drought resistance, construction scheduling of reservoirs, effective utilization of water resources and the like.
The prediction of the hydrologic disaster usually uses hydrologic prediction, but the existing hydrologic disaster prediction cannot make specific precautions according to specific categories and specific probabilities of occurrence of the hydrologic disaster, and some cases with smaller occurrence probabilities of the hydrologic disaster are processed according to the precautions with high occurrence probability, so that not only is the panic of residents easily caused, but also the resource utilization of society is wasted, and therefore, the hydrologic disaster prediction system based on data processing is provided.
In order to solve the above-mentioned defect, a technical scheme is provided.
Disclosure of Invention
The invention aims to solve the problems that the existing hydrologic disaster prediction cannot make specific preventive measures according to specific categories and specific probabilities of occurrence of hydrologic disasters, panic of residents is easy to cause, and waste is caused to social resource utilization.
The aim of the invention can be achieved by the following technical scheme:
a data processing-based hydrologic hazard prediction system, comprising:
the acquisition module is used for acquiring the places, types and probabilities of different hydrologic disaster categories;
the analysis module is used for updating the occurrence probability of different monitored hydrologic disaster categories into real-time probability in real time, presetting three probability limits, and respectively generating primary early warning, secondary early warning or tertiary early warning when one or more of the monitored real-time probabilities exceeds a first limit, a second limit or a third limit; the method is also used for recording and numbering the time points when the probability of the occurrence of the hydrologic disaster exceeds a preset limit value and transmitting the time points to the classification module;
the classification module is used for classifying the plurality of numbers according to different classification rules, carrying out historical prejudgment on the category, the specific time and the probability of occurrence of the hydrologic disaster by using the classified numbers, and establishing curves by using the numbers of the different categories;
the verification module is used for verifying the hydrologic disaster prevention force after receiving different early warning signals; verifying the hydrologic disaster prevention factors, calculating to obtain a hydrologic disaster prescriptive value, judging that the hydrologic disaster prevention factors do not reach the standard when the hydrologic disaster prescriptive value is lower than the preset prescriptive value, and performing corresponding optimization;
and the prevention module is used for receiving different early warning signals and carrying out corresponding prevention measures according to different hydrologic disaster types and different early warning grades.
Further, the specific operation steps of recording and establishing the number of the monitored time points when the probability of each occurrence of the hydrologic disaster exceeds the preset limit value by the analysis module are as follows:
the time points when the probability of each hydrologic disaster exceeds the preset limit value are marked by using a number L-NN-YY-RR-SS-D, wherein: l is the category of the probability of the hydrologic disaster exceeding a preset limit value, and B, X, G and F respectively refer to storm flood, snow melt flood, slush flood and storm surge; NN is two last two years of the monitored hydrologic disaster probability exceeding a preset limit value representing a time point, and is composed of any two digits between 00 and 99 and including 00 and 99; YY is the month of the monitored hydrologic disaster probability exceeding a preset limit value representing the time point, and consists of numbers ranging from 01 to 12 and including 01 and 12; RR is a specific day of month at a representative time point exceeding a preset limit value in the monitored probability of the hydrologic disaster, and consists of numbers ranging from 01 to 31 and including 01 and 31; SS is the specific hour of the day exceeding a preset limit time point in the monitored probability of a hydrographic disaster, consisting of numbers between 01 and 24 and including 01 and 24; and D is three probability limiting grades exceeding a preset limit value in the monitored hydrologic disaster probability, wherein 1, 2 and 3 represent a first limit value, a second limit value and a third limit value respectively.
Further, the classification rules in the classification module include a category classification rule, a year classification rule, a quarter classification rule, a month classification rule, a day classification rule and a restriction class classification rule:
and carrying out historical prejudgment on the category, the specific time and the probability of occurrence of the hydrologic disaster by utilizing the rule after the serial numbers of different categories are classified, and simultaneously, carrying out curve establishment through the serial numbers of different categories to judge the frequency and the trend of the hydrologic disaster.
Further, the specific operation steps of the verification module for verifying the hydrologic disaster prevention factors are as follows:
verifying the smoothness of information transmission, namely establishing a group of test information groups at a transmitting end, transmitting the test information groups to a plurality of receiving ends, recording the time of the plurality of receiving ends for receiving the test information groups, and calculating the time average value of the plurality of receiving ends for receiving the test information groups by using an average value calculation method;
performing power-off test on an outdoor power supply with risk in a low-lying zone of a monitoring area, namely performing power-on and power-off test by controlling a switch of the power supply, obtaining a plurality of power-off response times after the test, and calculating a mean value of the power-off response times of the plurality of power supplies by using a mean value calculation method;
and in the process of verifying the drainage flow of the drainage facilities in the monitoring area, the drainage tests are respectively carried out on other drainage facilities such as a drainage ditch, a sewer, a drainage river reach and the like, the drainage quantity of the drainage facilities is verified, and the total drainage quantity and the total drainage flow of the drainage facilities are obtained by utilizing a summation formula.
Further, the specific operation steps of judging the unqualified factors in the hydrologic disaster prevention factors and performing corresponding optimization by the verification module are as follows:
normalizing the obtained time average value of the test information sets, the obtained time average value of the power-off response of the power supplies and the total amount of the water in the water facilities to obtain a hydrologic disaster pre-degree value, comparing the obtained hydrologic disaster pre-degree value with a preset pre-degree value, and judging unqualified factors in hydrologic disaster prevention factors when the hydrologic disaster pre-degree value is lower than the preset pre-degree value;
when a plurality of receiving ends exist, the time for receiving the test information group is longer than the preset receiving time, marking an information transmission route of the receiving end, the time for receiving the test information is longer than the preset receiving time, and maintaining an abnormal transmission route;
when the power-off response time of the power supplies does not reach the standard, maintaining the low-lying power supplies with the power-off response time of the power supplies not reaching the preset requirement;
when the total amount of the sewage of the plurality of sewage facilities does not reach the standard, the existing sewage facilities are optimized or additional sewage facilities are added.
Further, the specific operation steps of the preventive measure of the preventive module are as follows:
when the first-level early warning is received, the category generating the first-level early warning is confirmed, and when the occurrence probability of storm flood, snow melt flood, slush flood or storm surge reaches a first limit, a primary storm flood precaution measure, a primary snow melt flood precaution measure, a primary slush flood precaution measure or a primary storm surge precaution measure is carried out;
when the second-level early warning is received, the category generating the second-level early warning is confirmed, and when the occurrence probability of storm flood, snow melt flood, slush flood or storm surge is determined to reach a second limit, a mid-level precaution measure of storm flood, a mid-level precaution measure of snow melt flood, a mid-level precaution measure of slush flood or a mid-level precaution measure of storm surge is carried out;
and when the three-level early warning is received, confirming the category generating the three-level early warning, and when the occurrence probability of storm flood, snow melt flood, ice slush flood or storm surge is confirmed to reach a third limit, carrying out final precaution measures of storm flood, snow melt flood, ice slush flood or storm surge.
Compared with the prior art, the invention has the beneficial effects that:
(1) According to the invention, the existing hydrologic forecast is used for collecting the occurrence place, type and probability of the hydrologic disaster, and the analysis module is used for generating different grades of early warning for different types of hydrologic disasters and probabilities, and the prevention module is matched for carrying out different grades of prevention measures for different early warning, so that the utilization of social resources is more reasonable, and the panic caused by low-probability hydrologic disasters to residents is avoided;
(2) According to the invention, the classification module can effectively monitor the historical occurrence of the hydrologic disasters or the situation of the higher probability of the occurrence of the hydrologic disasters in the area, the analysis module is used for numbering and classifying the numbers, the historical prejudgment is carried out on the types, the specific occurrence time and the occurrence probability of the hydrologic disasters according to the post-classification rules of the numbers of different types, and the curve establishment can be carried out on the numbers of different types, so that the frequency and the trend of the occurrence of the hydrologic disasters in the monitored area can be intuitively judged.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
fig. 1 is a general block diagram of the system of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present disclosure is for the purpose of describing particular embodiments only, and is not intended to be limiting of the disclosure. As used in the specification and claims of this disclosure, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should be further understood that the term "and/or" as used in the present disclosure and claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As shown in fig. 1, a hydrological disaster prediction system based on data processing includes an acquisition module, an analysis module, a classification module, a verification module and a prevention module;
the acquisition module is used for acquiring the place, type and probability of the occurrence of the hydrologic disaster; the hydrologic disaster consists of four categories of storm flood, snow melt flood, slush flood and storm surge;
monitoring the storm flood by monitoring the rainfall capacity of a monitoring area and the water level and water flow of the water body; predicting the weather conditions of the monitoring area by using a remote sensing satellite, so as to monitor the upcoming larger rainfall and obtain rainfall information of the monitoring area; the water condition stations arranged in different water bodies can actually measure the water level and water flow of the water bodies in real time, so that the probability and specific time and place of occurrence of storm flood are estimated; wherein the water body is composed of rivers, lakes, reservoirs or other water bodies;
the snow-melting flood is monitored, the monitoring area can be based on a foundation monitoring station, space-based patrol and space-based satellites are used for supplementing and extending, the snow area, the snow depth, the snow density, the water holding capacity and the snow surface freezing depth of the area are monitored in real time, the heat quantity of snow melting, the topography, the landform, the azimuth, the climate and the land use condition of a snow-melting field are monitored in real time, and an intelligent, synergistic and efficient three-dimensional monitoring network is provided for the snow-melting flood event of the monitoring area, so that the occurrence probability and the time place of the snow-melting flood are predicted;
monitoring the slush flood, analyzing the characteristics of the river reach places located in the monitored area according to the combination of the ice delivery quantity of the river flow meter and the meteorological conditions to obtain places threatening the river reach and the probability of the slush flood so as to predict;
the storm surge is monitored, and the probability and the place of the storm surge are predicted by matching the wind pressure gradient, the air pressure and the air whirl track of the monitored area with the water level of the preset point in the area.
The analysis module is used for receiving the place, type and probability of the occurrence of the hydrologic disaster acquired by the acquisition module, updating the probability in real time, wherein the monitored real-time probability consists of a storm flood occurrence probability, a snow melt flood occurrence probability, a slush flood occurrence probability and a storm tide occurrence probability, three probability limits are preset, namely a first limit value, a second limit value and a third limit value, the probabilities of the first limit value, the second limit value and the third limit value are sequentially increased, and when one or more of the monitored real-time probabilities exceeds the first limit value, a first-level early warning is generated; when one or more of the monitored real-time probabilities exceeds a second limit value, generating a second-level early warning; when one or more of the monitored real-time probabilities exceeds a third limit value, generating a third-level early warning;
the method is also used for recording the time points when the monitored probability of each occurrence of the hydrologic disaster exceeds the preset limit value, and marking the time points when the probability of each occurrence of the hydrologic disaster exceeds the preset limit value by using a number L-NN-YY-RR-SS-D, wherein: l is the category of the probability of the hydrologic disaster exceeding a preset limit value, and B, X, G and F respectively refer to storm flood, snow melt flood, slush flood and storm surge; NN is two last two years of the monitored hydrologic disaster probability exceeding a preset limit value representing a time point, and is composed of any two digits between 00 and 99 and including 00 and 99; YY is the month of the monitored hydrologic disaster probability exceeding a preset limit value representing the time point, and consists of numbers ranging from 01 to 12 and including 01 and 12; RR is the specific number of days of the monitored hydrologic disaster probability exceeding a preset limit value representing the time point, and consists of numbers ranging from 01 to 31 and including 01 and 31; SS is the specific hour of the day exceeding a preset limit time point in the monitored probability of a hydrographic disaster, consisting of numbers between 01 and 24 and including 01 and 24; d is three probability limiting grades exceeding a preset limit value in the monitored hydrologic disaster probability, wherein 1, 2 and 3 represent a first limit value, a second limit value and a third limit value respectively; the plurality of numbers are transmitted to the classification module.
The classification module is used for classifying the plurality of numbers according to different classification rules, wherein the classification rules comprise category classification rules, year classification rules, quarter classification rules, month classification rules, day classification rules and restriction class classification rules;
the class classification rule classifies the numbers with L being the same letter in the numbers into a storm flood class number, a snow melt flood class number, a slush flood class number and a storm surge class number;
the year classification rule classifies the numbers with NN being the same number in the plurality of numbers; the quarter classification rule classifies the numbers YY in four sections of 01-03, 04-06, 07-09 and 09-12 into four quarter numbers of spring, summer, autumn and winter;
the monthly classification rule is to classify the numbers of the RR in the three intervals of 01-10, 11-20 and 21-31 into three numbers of the last ten days, the middle ten days and the last ten days;
the daily classification rule classifies the serial numbers of SS in three intervals of 01-08, 09-16 and 17-24 into three serial numbers of morning, noon and evening;
the restriction class classification rule classifies the numbers D which are 1, 2 and 3 in the plurality of numbers into a first limit number, a second limit number and a third limit number;
after the classification module is used for classifying the plurality of numbers, the classification rules of the numbers of different categories can be used for carrying out historical prejudgment on the categories, the specific time of occurrence and the probability of occurrence of the hydrologic disasters, and the numbers of different categories can be also subjected to curve establishment, so that the frequency and the trend of the hydrologic disasters in the monitoring area can be judged intuitively.
The verification module is used for verifying the hydrologic disaster prevention strength after receiving the early warning signaling transmitted by the analysis module;
aiming at the verification of the hydrologic disaster prevention force, the information transmission smoothness can be verified, the verification method can be used for establishing a group of test information groups at a transmitting end, transmitting the test information groups to a plurality of receiving ends, recording the time of the plurality of receiving ends for receiving the test information groups, and calibrating the time as follows respectively
Where n is the number of multiple receivers, using the formula: />
Obtaining a time average value S of a plurality of receiving ends for receiving the test information group; time for receiving a plurality of receiving terminals to test information set +.>
Comparing the information transmission fluency with the preset receiving time, and completing verification of the information transmission fluency when the time of the plurality of receiving ends for receiving the test information set is within the preset receiving time; when there is a receiving end in the plurality of receiving ends that receives the test information group for a time longer than the preset receiving time, the time for receiving the test information isMarking an information transmission route of a receiving end which is longer than a preset receiving time, and maintaining an abnormal transmission route; performing secondary verification after maintenance is completed until the transmission speed of the abnormal transmission route is normal;
the outdoor power supply with risk in the low-lying area of the monitoring area is subjected to power-off test, namely the power-on and power-off test is performed by controlling the switch of the power supply, and a plurality of power-off response times are obtained after the test and are respectively calibrated as
And uses the formula: />
Obtaining the average value T of the power-off response time of the power supplies when the power-off response time of the power supplies is +.>
If the power failure control of the low-lying area is normal, the low-lying power supply which does not meet the preset requirement is maintained;
collecting the water discharge of the drainage facilities in the monitoring area, wherein the drainage facilities are formed by other drainage facilities such as a drainage ditch, a sewer and a drainage river reach, the other drainage facilities such as the drainage ditch, the sewer and the drainage river reach are respectively subjected to drainage test, the water discharge of a plurality of drainage facilities is respectively collected, and the water discharge is respectively calibrated as
L is the number of a plurality of sewage facilities, using the formula: />
Obtaining total drainage flow X of a plurality of drainage facilities, wherein the total drainage flow X of the plurality of drainage facilities is the drainage flow of the drainage facilities, comparing the total drainage flow X with preset drainage flow and judging whether the drainage requirements are met; when the preset drainage flow is not reached, optimizing the existing drainage facilities or adding additional drainage facilities to improve the drainage quantity so as to meet the preset requirements; />
Multiple receiving terminals to be collectedThe time average value S of the received test information set, the time average value T of the power-off response of the power supplies and the total quantity X of the water-down facilities are normalized, and are substituted into a formula:
obtaining a hydrologic disaster pre-degree value LD, wherein +.>
Respectively receiving a time average value preset weight coefficient of a test information group, a time average value preset weight coefficient of a plurality of power supply outage response time average values and a total water quantity preset weight coefficient of a plurality of water facilities by a plurality of receiving ends, and performing +_on>
The values are 1.5, 1.3 and 1.9 respectively; />
The correction factor is 0.986; comparing the obtained hydrologic disaster pre-degree value LD with a preset pre-degree value, judging whether the time of receiving the test information sets, the power-off response time of the power supplies and the total amount of the water in the water facilities are not up to standard when the hydrologic disaster pre-degree value LD is lower than the preset pre-degree value, and performing corresponding optimization to enable the hydrologic disaster pre-degree value LD to reach a preset pre-degree value standard.
The prevention module is used for receiving the primary early warning, the secondary early warning and the tertiary early warning generated by the analysis module;
when the first-level early warning is received, the category generating the first-level early warning is confirmed, and when the probability of occurrence of the storm flood reaches a first limit, a first-level precaution measure of the storm flood is carried out; when the probability of occurrence of the snow-melting flood reaches the first limit, performing primary prevention measures of the snow-melting flood; when the occurrence probability of the slush flood reaches the first limit value, performing a primary slush flood prevention measure; when the storm surge occurrence probability is determined to reach the first limit, performing a storm surge primary precaution measure;
when the second-level early warning is received, confirming the category of the second-level early warning, and when the probability of occurrence of the storm flood reaches the second limit, performing intermediate-level precaution measures of the storm flood; when the probability of the occurrence of the snow-melting flood reaches the second limit, performing mid-level preventive measures of the snow-melting flood; when the occurrence probability of the slush flood reaches the second limit value, performing a mid-level preventing measure of the slush flood; when the storm surge occurrence probability is determined to reach the second limit, performing a storm surge mid-level preventive measure;
when the third-level early warning is received, the category generating the third-level early warning is confirmed, and when the probability of occurrence of the storm flood reaches the third limit, advanced precaution measures of the storm flood are carried out; when the probability of occurrence of the snow-melting flood reaches the third limit, performing advanced prevention measures of the snow-melting flood; when the occurrence probability of the slush flood reaches the third limit value, performing a high-level slush flood prevention measure; when the storm tide occurrence probability is determined to reach the third limit, performing advanced storm tide prevention measures;
the method comprises the steps of carrying out primary early warning information transmission on different media by utilizing an information transmission route, enabling the media to carry out primary early warning information to public, wherein the primary early warning information consists of the occurrence probability of the storm flood, the approximate place and the time, carrying out inspection on the blocking condition of a sewer outlet in sewer measures in a monitoring area, avoiding water accumulation and disaster formation caused by blocking the sewer outlet in the storm, carrying out coverage prompt on residents in a low-lying residential area in the monitoring area, and carrying out short message prompt on mobile phone terminals of the resident in the part; the primary prevention measures of snow melt flood are that the areas with more snow are patrolled at the water outlet on the basis of the primary prevention measures of storm flood, and the hillsides with more snow are protected, so that debris flow is avoided; the method comprises the steps that a primary ice flood precaution measure is to collect weather and water conditions of a river reach on the basis of a primary storm flood precaution measure, and prepare corresponding ice melting; the primary storm surge precaution measures are that storm surge precaution measures are sent to the ship in the navigation of the monitoring area;
the method comprises the steps of screening silted river channels in cities on the basis of a storm flood primary precaution measure, filling flood prevention materials for residents in low-lying residential areas, wherein the flood prevention materials are portable flood prevention materials such as water baffles, flood prevention bags, water pumps and light enclosing walls, inspecting electric facilities at the street, and notifying maintenance personnel of timely maintenance if the situation that wires fall off is found; the snow-melting flood primary precaution measure is to store materials on the basis of the snow-melting flood primary precaution measure and the heavy rain flood intermediate precaution measure, wherein the materials comprise excavators, snow-shoveling vehicles, engineering vehicles, emergency lighting and the like; the method comprises the following steps that a slush flood mid-level preventive measure is used for carrying out slush flood early warning on mobile phone terminals on residents on two sides of a corresponding river channel on the basis of a slush flood primary preventive measure and a storm flood mid-level preventive measure; the mid-level precaution measure of storm surge is that on the basis of the mid-level precaution measure of storm surge of the primary precaution measure of storm surge, a signal for driving a ship into a port for preventing wind, sealing a cabin, setting down a sail as soon as possible and preparing flood control materials in a coastal area is sent to a receiving end of a ship crew;
the method comprises the following steps of informing residents in a low-lying residential area to transfer on the basis of a high-level storm flood precaution measure, dispersing people on river banks or other flood prevention facilities by broadcasting, temporarily closing low-lying areas such as subways, underground culverts, street-crossing tunnels, underground civil air defense projects and the like, optimizing the stored drainage facilities, adding additional drainage facilities to improve drainage, orderly powering off an outdoor power supply with risks in the low-lying areas of a monitoring area, and informing in advance before powering off; the snow-melting flood advanced precaution measure is to transmit transfer instructions to mountain residents in the monitored area on the basis of the snow-melting flood intermediate precaution measure and the storm flood advanced precaution measure, so that larger losses caused by the mountain floods are avoided; the advanced prevention measures of the ice flood are to find proper time to burst the curved narrow river reach or the formed ice dam river reach which are easy to form the ice blocking dam on the basis of the advanced prevention measures of the ice flood and the advanced prevention measures of the storm flood, so that the smooth discharging of the ice from the water is facilitated, and the operations of the migration and the arrangement of the beach area in the monitoring area, the divided Ling Hongou residents and the like are performed; the high-level storm preventive measure is to evacuate and transfer coastal residents in a monitored area on the basis of the high-level storm flood preventive measure, and disperse residents nearby facilities such as a sea pond embankment, a culvert gate, a wharf, a revetment and the like.
Specific:
at 15 PM on 26 months of 2021, predicting the weather condition of a monitoring area by using a remote sensing satellite through an acquisition module, and monitoring the upcoming larger rainfall to obtain rainfall information of the monitoring area; the water regime stations arranged in different water bodies in the area actually measure the water level and the water flow of the water bodies in real time, so that the occurrence probability of the storm flood is presumed to be higher, and the occurrence probability and specific time and place are obtained;
after determining the probability of occurrence of the regional storm flood and specific time and place, transmitting data to an analysis module, realizing real-time updating and changing the real-time probability of the regional storm flood for real-time transmission, comparing the real-time probability with three preset limits to obtain that the probability of occurrence of the regional storm flood is between a first limit value and a second limit value, and directly generating primary early warning;
the storm flood in this area generates the number B-21-05-26-15-1; the historical hydrologic disaster numbers of the area are called by utilizing the classification module, the classification rules of the numbers of different categories are utilized to carry out historical prejudgment on the categories, the specific time of occurrence and the occurrence probability of the hydrologic disaster, and the numbers of different categories can also be subjected to curve establishment, so that the frequency and the trend of the hydrologic disaster in the monitoring area can be intuitively judged;
after primary early warning is obtained, the hydrologic disaster prevention force of the area is verified by a verification module, the hydrologic disaster prevention factors are verified respectively, namely, the time average value of a plurality of receiving ends receiving the test information set, the time average value of a plurality of power-off response times and the total amount of water in a plurality of water-down facilities are tested respectively, the hydrologic disaster pre-degree value is calculated through normalization processing after the result is obtained, the calculated hydrologic disaster pre-degree value is compared with a preset pre-degree value, when the hydrologic disaster pre-degree value is lower than the preset pre-degree value, unqualified factors in the hydrologic disaster prevention factors are judged, and after the fact that the receiving ends with the time for receiving the test information set being longer than the preset receiving time exist in the plurality of receiving ends is judged, the information transmission route of the receiving ends with the time for receiving the test information set being longer than the preset receiving time is marked, the abnormal transmission route is maintained, and therefore the hydrologic disaster pre-degree value is confirmed to reach the preset pre-degree value standard;
the primary early warning is also used for transmitting to the prevention module, after the prevention module receives the primary early warning, and after the type of the hydrologic disaster occurring in the area is determined to be the type of the storm flood, primary prevention measures of the storm flood are carried out, the information transmission route is utilized for carrying out primary early warning on different media, so that the media carry out primary early warning information to the public, the primary early warning information consists of the occurrence probability of the storm flood, the approximate place and the time, the blocking condition of a sewer in sewer measures in the area is patrolled, the sewer is prevented from being blocked, the accumulated water is prevented from being in disaster when the storm is caused, the resident in the low-lying residential area of the monitoring area carries out coverage prompt, and the mobile phone terminal of the resident in the part is covered prompt.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (6)
1. A data processing-based hydrologic disaster prediction system, comprising:
the acquisition module is used for acquiring the places, types and probabilities of different hydrologic disaster categories;
the analysis module is used for updating the occurrence probability of different monitored hydrologic disaster categories into real-time probability in real time, presetting three probability limits, and respectively generating primary early warning, secondary early warning or tertiary early warning when one or more of the monitored real-time probabilities exceeds a first limit, a second limit or a third limit; the method is also used for recording and numbering the time points when the probability of the occurrence of the hydrologic disaster exceeds a preset limit value and transmitting the time points to the classification module;
the classification module is used for classifying the plurality of numbers according to different classification rules, carrying out historical prejudgment on the category, the specific time and the probability of occurrence of the hydrologic disaster by using the classified numbers, and establishing curves by using the numbers of the different categories;
the verification module is used for verifying the hydrologic disaster prevention force after receiving different early warning signals; verifying the hydrologic disaster prevention factors, calculating to obtain a hydrologic disaster prescriptive value, judging that the hydrologic disaster prevention factors do not reach the standard when the hydrologic disaster prescriptive value is lower than the preset prescriptive value, and performing corresponding optimization;
and the prevention module is used for receiving different early warning signals and carrying out corresponding prevention measures according to different hydrologic disaster types and different early warning grades.
2. The data processing-based hydrological disaster prediction system according to claim 1, wherein the specific operation steps of recording and numbering the time points when the probability of occurrence of the hydrological disaster exceeds the preset limit value by the analysis module are as follows:
the time points when the probability of each hydrologic disaster exceeds the preset limit value are marked by using a number L-NN-YY-RR-SS-D, wherein: l is the category of the probability of the hydrologic disaster exceeding a preset limit value, and B, X, G and F respectively refer to storm flood, snow melt flood, slush flood and storm surge; NN is two last two years of the monitored hydrologic disaster probability exceeding a preset limit value representing a time point, and is composed of any two digits between 00 and 99 and including 00 and 99; YY is the month of the monitored hydrologic disaster probability exceeding a preset limit value representing the time point, and consists of numbers ranging from 01 to 12 and including 01 and 12; RR is a specific day of month at a representative time point exceeding a preset limit value in the monitored probability of the hydrologic disaster, and consists of numbers ranging from 01 to 31 and including 01 and 31; SS is the specific hour of the day exceeding a preset limit time point in the monitored probability of a hydrographic disaster, consisting of numbers between 01 and 24 and including 01 and 24; and D is three probability limiting grades exceeding a preset limit value in the monitored hydrologic disaster probability, wherein 1, 2 and 3 represent a first limit value, a second limit value and a third limit value respectively.
3. The data processing-based hydrologic disaster prediction system according to claim 1, wherein the classification rules in the classification module include category classification rules, year classification rules, quarter classification rules, month classification rules, day classification rules and limit class classification rules;
and carrying out historical prejudgment on the category, the specific time and the probability of occurrence of the hydrologic disaster by utilizing the rule after the serial numbers of different categories are classified, and simultaneously, carrying out curve establishment through the serial numbers of different categories to judge the frequency and the trend of the hydrologic disaster.
4. The data processing-based hydrologic disaster prediction system according to claim 1, wherein the specific operation steps of the verification module for verifying hydrologic disaster prevention factors are as follows:
verifying the smoothness of information transmission, namely establishing a group of test information groups at a transmitting end, transmitting the test information groups to a plurality of receiving ends, recording the time of the plurality of receiving ends for receiving the test information groups, and calculating the time average value of the plurality of receiving ends for receiving the test information groups by using an average value calculation method;
performing power-off test on an outdoor power supply with risk in a low-lying zone of a monitoring area, namely performing power-on and power-off test by controlling a switch of the power supply, obtaining a plurality of power-off response times after the test, and calculating a mean value of the power-off response times of the plurality of power supplies by using a mean value calculation method;
and in the process of verifying the drainage flow of the drainage facilities in the monitoring area, the drainage test is respectively carried out on the drainage ditch, the drainage canal and the drainage river reach, the drainage quantity of the plurality of drainage facilities is verified, and the total drainage quantity and the total drainage flow of the plurality of drainage facilities are obtained by utilizing a summation formula.
5. The data processing-based hydrological disaster prediction system according to claim 1, wherein the specific operation steps of the verification module for judging the unqualified factors in the hydrological disaster prevention factors and performing the corresponding optimization are as follows:
normalizing the obtained time average value of the test information sets, the obtained time average value of the power-off response of the power supplies and the total amount of the water in the water facilities to obtain a hydrologic disaster pre-degree value, comparing the obtained hydrologic disaster pre-degree value with a preset pre-degree value, and judging unqualified factors in hydrologic disaster prevention factors when the hydrologic disaster pre-degree value is lower than the preset pre-degree value;
when a plurality of receiving ends exist, the time for receiving the test information group is longer than the preset receiving time, marking an information transmission route of the receiving end, the time for receiving the test information is longer than the preset receiving time, and maintaining an abnormal transmission route;
when the power-off response time of the power supplies does not reach the standard, maintaining the low-lying power supplies with the power-off response time of the power supplies not reaching the preset requirement;
when the total amount of the sewage of the plurality of sewage facilities does not reach the standard, the existing sewage facilities are optimized or additional sewage facilities are added.
6. The data processing-based hydrological disaster prediction system according to claim 1, wherein the preventive measures of the preventive module are specifically operated as follows:
when the first-level early warning is received, the category generating the first-level early warning is confirmed, and when the occurrence probability of storm flood, snow melt flood, slush flood or storm surge reaches a first limit, a primary storm flood precaution measure, a primary snow melt flood precaution measure, a primary slush flood precaution measure or a primary storm surge precaution measure is carried out;
when the second-level early warning is received, the category generating the second-level early warning is confirmed, and when the occurrence probability of storm flood, snow melt flood, slush flood or storm surge is determined to reach a second limit, a mid-level precaution measure of storm flood, a mid-level precaution measure of snow melt flood, a mid-level precaution measure of slush flood or a mid-level precaution measure of storm surge is carried out;
and when the three-level early warning is received, confirming the category generating the three-level early warning, and when the occurrence probability of storm flood, snow melt flood, ice slush flood or storm surge is confirmed to reach a third limit, carrying out final precaution measures of storm flood, snow melt flood, ice slush flood or storm surge.
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