CN111782638A - Geological disaster database establishment method based on big data - Google Patents
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Abstract
The invention discloses a geological disaster database building method based on big data, which comprises the following steps: acquiring regional geological information data by using big data service, wherein the geological information comprises natural geography, hydrological characteristics, weather conditions, geological structures, geological movement information and historical disaster records; analyzing the geological development condition of each area; aiming at the analysis condition, establishing an analysis model, and selecting a specific event factor according to the geological development condition of each region for analysis; establishing a relation between each development factor and geological influence, and storing the relation to form a geological disaster database, wherein the geological development condition comprises the influence distribution characteristics of rainfall on hydrology. According to the method and the device, the regional position geological information data on the network is obtained from more websites not limited to country related websites and APP by utilizing big data, so that data collection channels are widened, the real-time performance of the data is improved to a certain extent, and the accuracy of the data of the whole database is facilitated.
Description
Technical Field
The invention belongs to the technical field of geological disaster analysis, and particularly relates to a geological disaster database building method based on big data.
Background
Geological disasters refer to geological effects or phenomena formed under the action of natural or human factors, which cause losses to human life and property, and damage to the environment. Under the action of the internal power, the external power or the artificial geological power, the earth generates abnormal energy release, material movement, deformation and displacement of rock and soil bodies, abnormal change of the environment and the like, and the phenomena or processes of harming human lives and properties, living and economic activities or destroying resources and environments on which human beings live and develop are generated. Adverse geological phenomena are commonly called geological disasters, and refer to geological events that deteriorate geological environment, reduce environmental quality, directly or indirectly harm human safety, and cause losses for social and economic construction, caused by natural geological effects and human activities. Geological disasters are geological effects (phenomena) which are formed under the action of natural or human factors and damage and lose human lives, properties and environments. Such as collapse, landslide, debris flow, ground fissure, ground subsidence, rock burst, water burst in underground tunnel, mud burst, gas burst, spontaneous combustion of coal bed, loess collapse, rock-soil expansion, sandy soil liquefaction, land freeze-thaw, water loss and soil erosion, land desertification and swampiness, soil salinization, earthquake, volcano, geothermal damage, etc.
Disclosure of Invention
The invention aims to: the method for establishing the geological disaster database based on the big data is provided, the existing geological disaster prediction analysis technology is enriched, and the full and reliable data support is provided.
The technical scheme adopted by the invention is as follows:
a geological disaster database building method based on big data comprises the following steps:
step 1: acquiring regional geological information data by using big data service, wherein the geological information comprises natural geography, hydrological characteristics, weather conditions, geological structures, geological movement information and historical disaster records;
step 2: analyzing the geological development condition of each area;
and step 3: aiming at the analysis condition, establishing an analysis model, and selecting a specific event factor according to the geological development condition of each region for analysis;
and 4, step 4: and establishing a relation between each development factor and the geological influence, and storing the relation to form a geological disaster database.
The geological development condition comprises the distribution characteristics of influence of rainfall on hydrology, the distribution characteristics of change of hydrology on natural geography, the distribution characteristics of influence of geological motion on structures and the distribution characteristics of mutual chain lock influence.
Wherein the step of obtaining regional geological information data using big data services comprises: acquiring information data from a country-related website;
acquiring geological information of other websites including the APP and the position of the region by utilizing big data;
and analyzing the acquired information content, and classifying the corresponding geological information and dividing time.
Wherein, the analysis model established in the step 3 is a multivariate statistical model, and a Logistic regression (Logistic) model is used.
In the step 4, while establishing the relation between each development factor and the geological influence, labels are formed on the corresponding areas, wherein the labels comprise easy-to-send, relatively easy-to-send, common, less-to-send and difficult-to-send, and represent the occurrence difficulty of geological disasters.
Wherein the label forming step comprises: counting according to the historical disaster occurrence frequency to obtain a disaster occurrence frequency change curve taking years as units;
counting according to the frequency of occurrence of each month in a annual unit to obtain a disaster occurrence frequency change curve taking a month as a unit;
and combining the two change curves to classify the corresponding labels.
Before the classification is carried out to the corresponding label, the classification is compared with a preset frequency reference line, if the whole curve is higher than the reference line, the corresponding time is judged to be in a more hair-prone state, the amplitude higher than the reference line reaches a preset upper limit standard and is judged to be in a more hair-prone state, if the whole curve is lower than the reference line, the corresponding time is judged to be in a less hair-prone state, and the amplitude lower than the reference line reaches a preset lower limit standard and is judged to be in a less hair-prone state.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. in the invention, multiple modes are provided for inquiring, managing, counting and analyzing disaster points, the system carries out subarea identification on the geological disaster easy-to-send degree of the disaster points, determines the prevention key and key prevention areas, and is a monitoring network which is specially combined with the whole group, thereby realizing short message early warning and expert remote real-time consultation, assisting all levels of people governments to formulate geological disaster prevention schemes, disaster prevention plans and risk avoidance relocation engineering plans, playing the benefits of disaster reduction and prevention and protecting the life and property safety of people.
2. According to the invention, the database establishing process is simplified, and the new data can be updated and brought into the database in time when being generated, so that the existing actual situation can be ensured to be dealt with in time.
3. According to the method and the device, the regional position geological information data on the network is obtained from more websites not limited to country related websites and APP by utilizing big data, so that data collection channels are widened, the real-time performance of the data is improved to a certain extent, and the accuracy of the data of the whole database is facilitated.
Drawings
FIG. 1 is a schematic diagram of a database establishment process according to the present invention;
FIG. 2 is a system block diagram of the present invention;
FIG. 3 is a modular block diagram of an automatic update module of the present invention;
fig. 4 is a schematic view of the working process of the main control system of the present invention.
The labels in the figure are: 1. a master control system; 10. calling a request module; 20. a master database; 30. a maintenance module; 40. an examination module; 50. an encryption module; 60. an automatic update module; 70. a database of points; 80. a manual processing module; 601. a source data acquisition unit; 602. a data extraction unit; 603. a data classification unit; 604. and a data transmission unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention; the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance; furthermore, unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, as they may be fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Referring to fig. 1 to 4, a method for establishing a geological disaster database based on big data includes the following steps:
step 1: acquiring regional geological information data by using big data service, wherein the geological information comprises natural geography, hydrological characteristics, weather conditions, geological structures, geological movement information and historical disaster records;
step 2: analyzing geological development conditions of each region, including influence distribution characteristics of rainfall on hydrology, change distribution characteristics of hydrology on natural geography, influence distribution characteristics of geological motion on structures and mutual chain lock influence distribution characteristics;
and step 3: aiming at the analysis condition, establishing an analysis model, and selecting a specific event factor according to the geological development condition of each region for analysis;
and 4, step 4: and establishing a relation between each development factor and the geological influence, and storing the relation to form a geological disaster database.
The step of obtaining regional geological information data using big data service comprises: acquiring information data from a country-related website;
acquiring geological information of other websites including the APP and the position of the region by utilizing big data;
and analyzing the acquired information content, and classifying the corresponding geological information and dividing time.
The analysis model established in the step 3 is a multivariate statistical model, a Logistic regression (Logistic) model is used, the Logistic model is a model frequently used in the two-classification dependent variable analysis, a maximum likelihood estimation and maximum parameter estimation method is adopted, the classification dependent variable and the classification independent variable can be subjected to regression modeling, and a result is provided in the form of event occurrence probability according to the standard for testing the regression model and the regression parameters.
In the step 4, while establishing the relation between each development factor and the geological influence, a label is formed for the corresponding area, the label comprises easy sending, relatively easy sending, common, less sending and difficult sending, and represents the occurrence difficulty of the geological disaster, and the label forming step comprises the following steps: counting according to the historical disaster occurrence frequency to obtain a disaster occurrence frequency change curve taking years as units;
counting according to the frequency of occurrence of each month in a annual unit to obtain a disaster occurrence frequency change curve taking a month as a unit;
and combining the two change curves to classify the corresponding labels.
And before the label is classified to the corresponding label, comparing the label with a preset frequency reference line, if the whole curve is higher than the reference line, judging that the corresponding time is in a more hair-prone state, if the amplitude of the curve higher than the reference line reaches a preset upper limit standard, judging that the corresponding time is in a less hair-prone state, if the whole curve is lower than the reference line, judging that the amplitude of the curve lower than the reference line reaches a preset lower limit standard, and judging that the corresponding time is in a less hair-prone state.
The working system of the geological disaster database based on the big data comprises a main control system 1, a calling request module 10, a database, a maintenance module 30, an examination module 40, an encryption module 50, an automatic updating module 60 and a manual processing module 80 which are in communication control connection with the main control system, wherein the calling request module 10 is used for generating a data request to the main control system 1 to acquire data when a third party uses information data in the database, and direct acquisition is not adopted to ensure the effective and safe data of the database; the maintenance module 30 is used for daily maintenance and management of the system; the auditing module 40 is used for auditing the received third party request information and the identity of the third party and determining whether to feed back the requested data; the encryption module 50 is used for encrypting the database to ensure the security of the database; the automatic updating module 60 is used for automatically updating the contents of the database to ensure the timeliness of the data; the manual processing module 80 is used for processing some problems by manual work;
the database comprises a main database 20 and an auxiliary database 70, wherein the main database 20 is a formal database which can be directly obtained for use, and the auxiliary database 70 is used for temporary data storage of the automatic updating module 60 and temporarily stores the unprocessed data received by updating;
the automatic updating module 60 comprises a source data acquiring unit 601, a data extracting unit 602, a data classifying unit 603 and a data transmitting unit 604, wherein the source data acquiring unit 601 acquires geology-related information data from a network by using big data; the data extraction unit 602 extracts useful information; the data classification unit 603 classifies the extracted information according to the database requirements; the data transmission unit 604 updates the confirmed data to the master database 20;
the working method of the working system of the geological disaster database based on the big data comprises the following steps:
the method comprises the steps that a demand end sends a data demand request to a master control system, and demand request information comprises specific data types and demand party identity information;
the checking module in the master control system checks the identity and searches whether relevant data exist in the database according to the data category of the request information;
after the examination is passed, the corresponding data is transferred from the main database to the demand side, if the requested data category is not retrieved in the database, a prompt without related information is directly fed back, the examination has timeliness, after the data is transferred to the demand side demand data, the demand side does not have further operation, timing is started, after the timing is over for three minutes, the demand side operates again, then the examination is performed again, meanwhile, when the demand side actively breaks off the link with the main control system, the examination is immediately invalid, and when the demand side is connected again, the examination is performed again, so that the system space occupation is reduced, and the system smoothness during use is improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. A geological disaster database building method based on big data is characterized by comprising the following steps:
step 1: acquiring regional geological information data by using big data service, wherein the geological information comprises natural geography, hydrological characteristics, weather conditions, geological structures, geological movement information and historical disaster records;
step 2: analyzing the geological development condition of each area;
and step 3: aiming at the analysis condition, establishing an analysis model, and selecting a specific event factor according to the geological development condition of each region for analysis;
and 4, step 4: and establishing a relation between each development factor and the geological influence, and storing the relation to form a geological disaster database.
2. The method as claimed in claim 1, wherein the geological development includes distribution of influence of rainfall on hydrology, distribution of change of hydrology on natural geography, distribution of influence of geological motion on structure, and distribution of mutual chain lock influence.
3. The big-data-based geological disaster database building method according to claim 1, wherein said step of obtaining regional geological information data using big data service comprises: acquiring information data from a country-related website;
acquiring geological information of other websites including the APP and the position of the region by utilizing big data;
and analyzing the acquired information content, and classifying the corresponding geological information and dividing time.
4. A method for building a big data based geological disaster database according to claim 1, wherein said analytical model built in step 3 is a multivariate statistical model, using Logistic regression (Logistic) model.
5. The method for building a geological disaster database based on big data as claimed in claim 1, wherein in step 4, while establishing the relationship between each development factor and geological influence, labels are formed for the corresponding regions, and the labels comprise easy-to-send, easier-to-send, ordinary, short-to-send and difficult-to-send, and represent the difficulty of occurrence of geological disaster.
6. The big-data-based geological disaster database building method according to claim 5, wherein said label forming step comprises: counting according to the historical disaster occurrence frequency to obtain a disaster occurrence frequency change curve taking years as units;
counting according to the frequency of occurrence of each month in a annual unit to obtain a disaster occurrence frequency change curve taking a month as a unit;
and combining the two change curves to classify the corresponding labels.
7. The method as claimed in claim 6, wherein the classification is performed before the corresponding label, and compared with a preset frequency reference line, if the curve is higher than the reference line as a whole, the corresponding time is determined to be more likely to occur, if the amplitude of the curve higher than the reference line reaches a preset upper limit standard, the corresponding time is determined to be less likely to occur, if the curve is lower than the reference line as a whole, the corresponding time is determined to be less likely to occur, and if the amplitude of the curve lower than the reference line reaches a preset lower limit standard, the corresponding time is determined to be less likely to occur.
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