CN115965246A - Early warning analysis method for karst collapse disaster - Google Patents
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Abstract
The invention discloses an early warning analysis method for karst collapse disasters, which relates to the technical field of early warning and adopts the steps of firstly, acquiring geological data information through a data monitoring module; comparing or matching the data information monitored by the data monitoring module with standard data information in a database through a data evaluation module, and realizing variable selection and parameter estimation influencing the occurrence of karst collapse disasters through a Multi-feature elastic Multi-FEA algorithm model; and thirdly, analyzing the occurrence risk of the karst collapse disaster through an early warning analysis module, wherein the early warning analysis module comprises an early warning analysis main control module, and an alarm diagnosis module, a CART alarm classification module and an alarm output module which are connected with the early warning analysis main control module. The invention greatly improves the early warning and analyzing capability of the karst collapse disaster.
Description
Technical Field
The invention relates to the technical field of information alarm, in particular to an early warning analysis method for karst collapse disasters.
Background
Karst collapse (karst collapse) is a karst cave, an overlying sediment and underground water, and forms a solid, liquid and gas three-phase mechanical balance system, the underground water level changes to a certain extent, the balance is destroyed, the overlying loose sediment suddenly collapses to form a conical collapse pit with a large upper part and a small lower part. Karst collapse is a specific geological disaster type in a karst area, and in recent years, with the rapid development of national economic construction, the development of land resources, water resources and mineral resources in the karst area is continuously enhanced, so that the problem of karst collapse caused by the development is increasingly serious. How to carry out multi-parameter monitoring and early warning to prevent and control the karst collapse disaster risk becomes a problem to be solved urgently.
Related technical researches also exist in the prior art, for example, patent No. CN201921993918.5 discloses a karst collapse multi-parameter monitoring and early warning test system, and the existing researches include a karst collapse simulation system, a groundwater parameter monitoring system and a soil body deformation monitoring system, but are still imperfect in real-time performance of karst collapse disaster early warning, low in automation degree and capable of early warning and analyzing the karst collapse disasters.
Disclosure of Invention
Aiming at the technical defects, the invention discloses an early warning analysis method of karst collapse disasters, which can realize the real-time acquisition and analysis of geological data information, adopts a network geological information module WEBGIS and a hypertext transfer protocol (HTTP) real-time transmission module to realize the real-time acquisition and transmission of the geological data information, adopts a Multi-feature elastic algorithm Multi-FEA to construct a data model to ensure the accuracy and speed of the early warning analysis, and greatly improves the real-time performance of the early warning while ensuring the early warning accuracy.
In order to solve the technical problem, the invention adopts the following technical scheme:
an early warning analysis method for karst collapse disasters comprises the following steps:
acquiring geological data information through a data monitoring module, wherein the geological data information comprises underground water level information, flood collapse information, gravity collapse information, earthquake collapse information, cap soil information or opening karst parameter data information;
the data monitoring module comprises a main control unit, and a data dynamic acquisition module, an information filtering module, a data storage module and a data transmission module which are connected with the main control unit, wherein the data dynamic acquisition module is used for acquiring various geological data information causing karst collapse disasters in real time through a network geological information module WEBGIS, automatically arranging and managing the acquired geological data information in a light and heavy emergent queue mode through an active queue management algorithm model, updating the acquired geological data information in real time, controlling and managing a database SQL Server through the main control unit by the data storage module to store the geological data information, and transmitting the acquired karst collapse disaster information to other terminals in real time; the information filtering module is used for screening parameters influencing karst collapse disaster information analysis so as to improve data analysis capability; the data transmission module is used for transmitting data information;
comparing or matching the data information monitored by the data monitoring module with standard data information in a database through a data evaluation module, and realizing variable selection and parameter estimation influencing the occurrence of karst collapse disasters through a Multi-feature elastic Multi-FEA algorithm model;
and thirdly, analyzing the occurrence risk of the karst collapse disaster through an early warning analysis module, wherein the early warning analysis module comprises an early warning analysis main control module, and an alarm diagnosis module, a CART alarm classification module and an alarm output module which are connected with the early warning analysis main control module.
As a further technical scheme of the invention, the method for arranging the geological data information by the active queue management algorithm model comprises the following steps:
(S1) marking underground water level information, flood-induced collapse information, gravity-induced collapse information, earthquake-induced collapse information, cap soil information or open karst parameter data information according to causes caused by karst collapse disasters; dividing the early warning level of karst collapse disasters;
(S2) through 24-hour uninterrupted data information transmission, real-time monitoring of the actually acquired karst collapse disaster data volume through a queuing management change rule function, wherein the queuing management change rule function expression is as follows:
wherein ,
function expression representing a queue management change law, <' > or>
Represents all geological data information change functions between 9>
Represents all geological data information change functions between 20>
Represents a karst collapse disaster data change rule fluctuation function and is used for judging whether the karst collapse disaster data change rule fluctuation function is satisfied or not>
Represents the weight change of karst collapse disaster management data and is used for judging whether the karst collapse disaster management data is matched with the karst collapse disaster management data>
Represents the number of karst collapse disaster data information transfer batches and is combined with the karst collapse disaster data transfer batches>
and />
Representing the karst collapse disaster data transfer information fluctuation amount, and then judging whether the karst collapse disaster data transfer information fluctuation amount is greater than or equal to>
The information transmission times of karst collapse disaster data are represented, m represents the type of the karst collapse disaster data information, and R represents the effective rate of the karst collapse disaster data information;
and (S3) the karst collapse disaster geological data information output by the queuing management change rule function is represented by a dynamic curve, and the change trend of the karst collapse disaster management data is expressed in an image and visual mode.
As a further technical scheme of the invention, the information filtering module is provided with an ERP cloud communication module and filtering parameters connected with the ERP cloud communication module.
As a further technical scheme of the invention, the method for realizing variable selection and parameter estimation of karst collapse disaster occurrence by using the Multi-feature elastic Multi-FEA algorithm model comprises the following steps:
establishing a karst collapse disaster data information multi-feature information input model to be recorded as
，/>
For N multi-characteristic geological information parameter data pairs, wherein>
For multi-characteristic information argument parameters, <' >>
，/>
For a multi-characteristic information dependent variable parameter, <' >>
And/or>
The output functional relation expression is as follows:
in the formula (2), the first and second groups,
independent variable parameter for causing karst collapse disaster>
Is influenced by>
In order to cause a karst cave-in disaster, an independent variable parameter>
For a dependent variable parameter which causes a karst collapse disaster, is selected>
For a random distractor term>
Normal effects due to natural geological changes; wherein->
Parameter for multiple characteristic information argument>
The matrix of weight values of (a) is,
the multi-characteristic information dependent variable parameter is greater than or equal to the multi-characteristic information independent variable parameter matrix>
The output function formula of (a) is: />
In the formula (3), the first and second groups,
is a target value matrix->
For a matrix of characteristic values>
Is an estimated value matrix;
screening multiple characteristic parameters causing karst collapse disasters according to the state of the network communication system, and setting parameter threshold values causing the karst collapse disasters
Multivariate characteristic independent variable parameter causing karst collapse disaster>
And correspondingly, the multivariate characteristic independent variable parameter matrix which causes the karst collapse disaster->
Variables considered to be unaffected are deleted from the parametric model to improve selectionSpeed, parameter threshold value which causes a karst collapse disaster->
The output function is formulated as:
estimation value of influence of parameter indexes on karst collapse disaster
The output function formula of (a) is:
in the formula (5), the first and second groups,
in order to cause a karst cave-in disaster, an argument parameter matrix>
For a dependent variable parameter matrix which causes a karst collapse disaster, <' > H>
Independent variable parameter for causing karst collapse disaster>
Is used to make a karst collapse disaster total impact estimate->
The output function formula of (a) is:
in the case of the formula (6),
is the number of data pairs, is greater or less>
For an argument parameter which causes a karst collapse disaster, is selected>
Dependent variable parameters for causing karst cave-in disasters, in combination with a selection of parameters for determining the number of karst cave-in disasters>
For normal effects caused by natural geological changes>
The estimated value is influenced by parameter indexes causing karst collapse disasters.
As a further technical scheme of the invention, the alarm condition diagnosis module realizes karst collapse disaster information calculation through an FPGA device, and an EPF10K20TC144-4 chip is adopted by the FPGA device.
As a further technical scheme, the karst collapse disaster early warning level is divided into a level I early warning level, a level II early warning level, a level III early warning level, a level IV early warning level and a level V early warning level;
early warning at the I level: a reminding level, wherein the possibility of disaster occurrence is very small within 24 hours, and the group survey and group prevention inspection of important geological disaster hidden danger points is started;
II-level early warning: a reminding level, wherein the possibility of disaster occurrence is low within 24 hours, and important geological disaster hidden danger points are monitored within 24 hours within forecast and early warning time;
III-level early warning: the method comprises the following steps of (1) paying attention, starting group survey and group defense of hidden danger points of geological disasters within 24 hours, monitoring and taking defense measures within 24 hours, and reminding residents, factories, mines, schools, enterprises and public institutions near the places where disasters are easy to occur to pay close attention to weather forecast to prevent weather from suddenly deteriorating;
IV-level early warning: the early warning level is that in 24 hours, the possibility of disaster occurrence is high, a temporary avoiding scheme for residents in a region threatened by a potential hazard point of a geological disaster is started, outdoor operation near the place where the disaster easily occurs is suspended, and each relevant unit on-duty commander goes to the post to prepare emergency measures, organize emergency team, transfer residents in a dangerous zone and pay close attention to the change of rain condition;
and V-level early warning: an alarm level, wherein within 24 hours, the probability of disaster occurrence is high, and a temporary avoidance scheme for residents in an unstable dangerous slope threat area is started; residents, students, factories, mines, enterprises and public institutions personnel near the location where the disaster is easy to happen are emergently evacuated, relevant roads are closed, and the organization personnel prepare for emergency rescue;
as a further technical scheme of the invention, the SQL Server database is used for storing, browsing, editing, inquiring, outputting and modeling geological data information parameters, and the geological data information is controlled and recorded into the SQL Server database through the main control unit.
As a further technical scheme of the invention, the working method of the CART alarm classification module comprises the following steps: the geological data information parameters are set as a data set D, the early warning analysis module divides the data set D into 4 categories of groundwater water level amplitude, groundwater flow velocity, groundwater water chemistry characteristics and groundwater turbidity by adopting a method of combining a CART algorithm and integrated learning, and the probability that the geological data information parameters belong to the kth category is
，/>
The output formula of the Gini index of the probability distribution is as follows:
in the formula (7), the first and second groups,
for the probability that the geological data information parameter belongs to the kth category, < >>
The output formula of the Gini index of the geological data information parameter data set D is as follows:
in the formula (8), the first and second groups,
represents the number of data belonging to the category k in the data set D, is greater than or equal to>
The geological data information parameter dataset D is divided into 4 sub-datasets based on the characteristic a>
，/>
，/>
，/>
The output formula of the Gini index is as follows:
dividing a data set into n sub-intervals
In any interval>
The output produced->
The functional formula is:
in the formula (10), the first and second groups,
is interval->
On all>
Corresponding->
Is based on the mean value of>
In order to cause a karst cave-in disaster, an independent variable parameter>
Dependent variable parameters for causing a karst cave-in disaster, wherein>
Risk loss->
The output function formula of (a) is:
in the case of the formula (11),
for an argument parameter which causes a karst collapse disaster, is selected>
Is a dependent variable parameter causing karst collapse disasters.
The invention is different from the prior art and has the following positive and beneficial effects: the method comprises the steps that geological data information is obtained through a data monitoring module, the data information monitored by the data monitoring module is compared or matched with standard data information in a database through a data evaluation module, and variable selection and parameter estimation which influence the occurrence of karst collapse disasters are achieved through a Multi-feature elastic Multi-FEA algorithm model; the karst collapse disaster occurrence risk is analyzed through the early warning analysis module, the early warning analysis module comprises an early warning analysis main control module, an alarm condition diagnosis module, a CART alarm condition classification module and an alarm condition output module which are connected with the early warning analysis main control module, real-time collection and analysis of geological data information can be achieved, real-time collection and transmission of the geological data information are achieved through a network geological information module WEBGIS and HTTP real-time transmission technology, accuracy and speed of early warning analysis are guaranteed through a Multi-feature elastic algorithm Multi-FEA construction data model, and early warning instantaneity is greatly improved while early warning accuracy is guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without inventive labor, wherein:
FIG. 1 is a schematic diagram of an overall architecture of an early warning analysis method for karst collapse disasters according to the present invention;
fig. 2 is a schematic structural diagram of a data monitoring module in the karst collapse disaster early warning analysis method of the present invention;
FIG. 3 is a schematic diagram of a model principle architecture of a Multi-feature elastic algorithm Multi-FEA in the karst collapse disaster early warning analysis method of the invention;
fig. 4 is a schematic diagram of a model principle framework of a CART algorithm and an ensemble learning method in the early warning analysis method of karst collapse disasters.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, and it should be understood that the embodiments described herein are merely for purposes of illustration and explanation, and are not intended to limit the present invention.
As shown in fig. 1 to 4, a method for early warning and analyzing karst collapse disaster includes the following steps:
acquiring geological data information through a data monitoring module, wherein the geological data information comprises underground water level information, flood-induced collapse information, gravity-induced collapse information, earthquake-induced collapse information, cap soil information or opening karst parameter data information; in a specific embodiment, the method is not limited to the above method, and further comprises various data information for causing karst collapse disasters;
the data monitoring module comprises a main control unit, and a data dynamic acquisition module, an information filtering module, a data storage module and a data transmission module which are connected with the main control unit, wherein the data dynamic acquisition module is used for acquiring various geological data information causing karst collapse disasters in real time through a network geological information module WEBGIS, automatically arranging and managing the acquired geological data information in a light and heavy emergent queue mode through an active queue management algorithm model, updating the acquired geological data information in real time, controlling and managing a database SQL Server through the main control unit by the data storage module to store the geological data information, and transmitting the acquired karst collapse disaster information to other terminals in real time; the information filtering module is used for screening parameters influencing karst collapse disaster information analysis so as to improve data analysis capability; the data transmission module is used for transmitting data information;
comparing or matching the data information monitored by the data monitoring module with standard data information in a database through a data evaluation module, and realizing variable selection and parameter estimation influencing karst collapse disaster through a Multi-feature elastic Multi-FEA algorithm model; the variable selection and parameter estimation can be the change of external environment factors such as weather variables, environment variables and the like in a specific embodiment, and the parameter estimation is also parameter information influencing the prediction of karst collapse disasters;
and thirdly, analyzing the occurrence risk of the karst collapse disaster through an early warning analysis module, wherein the early warning analysis module comprises an early warning analysis main control module, and an alarm diagnosis module, a CART alarm classification module and an alarm output module which are connected with the early warning analysis main control module.
In the embodiment, the web geological information module webbis realizes real-time acquisition, management and storage of geological data information through an internal and external embedded hypertext transfer HTTP standard application system, and tracks the displacement and direction of karst collapse in real time in combination with a web deployment system to improve the early warning speed of the karst collapse disasters, and the work tasks of the hypertext transfer HTTP real-time transmission module include synchronous refreshing of a client timer, asynchronous transmission based on AJAX and Push of a server.
In a specific embodiment, a universal gateway interface development technology in a network geological information module WEBGIS is selected to introduce user information into a data monitoring system, corresponding internal operation inspection data is matched, system monitoring and control operation is performed on the premise that the landslide data are completely collected, the redundancy of the internal landslide data is reduced, and meanwhile, a central deployment system with strong network connectivity is combined to track the displacement and direction of the landslide to achieve timely updating of geological states.
In the above embodiment, the SQL Server database is configured to store, browse, edit, query, output, and model geological data information parameters, and the geological data information is controlled and entered into the SQL Server database through the main control unit.
In an embodiment, the SQL Server database is an extensible, high-performance database management system designed for distributed client/Server computing, which is organically integrated with windows nt.
In the above embodiment, the data monitoring module may also be a satellite data information transmission, high-altitude remote sensing, or other collection mode.
In a specific embodiment, the method for arranging the geological data information by the active queue management algorithm model comprises the following steps:
(S1) marking underground water level information, flood collapse information, gravity collapse information, earthquake collapse information, cap soil information or opening karst parameter data information according to the cause of karst collapse disasters; dividing the early warning level of karst collapse disasters;
in this step, since there are many data information causing the karst collapse disaster, the specific embodiment is not limited to the above embodiment, and various data information in the above embodiment can be listed respectively, or data information with a relatively large influence degree can be identified by different data information, so as to improve the identification degree and the capability of different data information;
(S2) through 24-hour uninterrupted data information transmission, the actually acquired karst collapse disaster data volume is monitored in real time through a queuing management change rule function, and the queuing management change rule function expression is as follows:
wherein ,
function expression representing a queue management change law, based on a queue management change rule>
Represents all geological data information change functions between 9>
Represents all geological data information change functions between 20>
A fluctuation function representing the change rule of karst collapse disaster data, based on the karst collapse disaster data and the karst collapse disaster data>
Represents weight change of karst collapse disaster management data and is combined with>
Represents the number of karst collapse disaster data information transfer batches and is used for judging whether the karst collapse disaster data information transfer batches are matched with the karst collapse disaster data information transfer batches or not>
and />
Representing the karst collapse disaster data transfer information fluctuation amount, and then judging whether the karst collapse disaster data transfer information fluctuation amount is greater than or equal to>
Representing the number of times of information transmission of karst collapse disaster data, mThe information type of the karst collapse disaster data is represented, and R represents the effective rate of the karst collapse disaster data information;
in the step, the queuing management change rule function expression can reflect the factors of the data information causing the lava collapse to a certain extent, and the data information is effectively expressed through a macroscopic data function, so that the capability of monitoring the data information continuously for 24 hours is improved. In order to improve data calculation capacity, the functions are divided into different functions to be represented, wherein all geological data information change functions between 9. All geological data information change functions between 9. The weight change of the karst collapse disaster management data indicates that when different data information is measured, measurement errors are easily caused by the difference of various information such as positions, spaces, structures and the like in each detection. Because the data information inducing factors of the karst collapse disaster are many, in a specific embodiment, the specific embodiment is not necessarily completely contained, and the key factors are given in the invention;
and (S3) the karst collapse disaster geological data information output by the queuing management change rule function is represented by a dynamic curve, and the change trend of the karst collapse disaster management data is expressed in an image and visual mode.
In the specific embodiment, when different parameter data information is expressed by micro data information, macro data information is converted into micro data information for analysis, and the data information analysis and application capability can be improved.
In the above embodiment, the information filtering module is provided with an ERP cloud communication module and a filtering parameter connected with the ERP cloud communication module. For example, in order to improve the data information computing capacity, the ERP cloud communication module is arranged to improve the interaction and communication capacity of the data information, and the filtering parameters reflect different karst collapse information so as to improve the data information application and computing capacity.
In a further embodiment, the method for realizing variable selection and parameter estimation of karst collapse disaster occurrence by using the Multi-feature elastic Multi-FEA algorithm model comprises the following steps:
establishing a karst collapse disaster data information multi-feature information input model to be recorded as
，/>
For N multi-characteristic geological information parameter data pairs, wherein>
For multi-characteristic information argument parameters>
，/>
For a multi-characteristic information dependent variable parameter, <' >>
And/or>
The output functional relation expression of (1) is as follows:
in the formula (2), the first and second groups of the chemical reaction are represented by the following formula,
independent variable parameter for causing karst collapse disaster>
Is influenced by>
For an argument parameter which causes a karst collapse disaster, is selected>
For a dependent variable parameter which causes a karst collapse disaster, is selected>
Is a random disturbing term, is selected>
Normal effects due to natural geological changes; wherein->
Argument parameter for multi-characteristic information>
Based on the weight value matrix, is greater than or equal to>
The multi-characteristic information dependent variable parameter is greater than or equal to the multi-characteristic information independent variable parameter matrix>
The output function formula of (a) is:
in the formula (3), the first and second groups,
is a target value matrix->
For a matrix of characteristic values>
Is an estimated value matrix;
screening multiple characteristic parameters causing karst collapse disasters according to the state of the network communication system, and setting parameter threshold values causing the karst collapse disasters
Multivariate characteristic independent variable parameter causing karst collapse disaster>
And correspondingly, the multivariate characteristic independent variable parameter matrix which causes the karst collapse disaster->
Considered as non-influencing variables are deleted from the parametric model to increase the selection speed, resulting in a parameter threshold ≥ for a karst collapse disaster>
The output function is formulated as:
estimation value of influence of parameter indexes on karst collapse disaster
The output function formula of (a) is:
in the formula (5), the first and second groups of the chemical reaction are represented by the following formula,
for the argument parameter matrix that causes a karst collapse disaster, <' >>
For a dependent variable parameter matrix which causes a karst collapse disaster, <' > H>
Independent variable parameter for causing karst collapse disaster>
Is used to make a karst collapse disaster total impact estimate->
The output function of (a) is formulated as: />
In the case of the formula (6),
is the number of data pairs, is greater or less>
For an argument parameter which causes a karst collapse disaster, is selected>
For a dependent variable parameter which causes a karst collapse disaster, is selected>
For normal effects caused by natural geological changes>
The evaluation value is influenced by parameter indexes causing karst collapse disasters.
In the implementation process, a Multi-feature elastic Multi-FEA algorithm model is installed in a computer, a Multi-feature elastic Multi-FEA algorithm model function is set, variable selection and parameter estimation of karst collapse disasters are achieved through a Multi-feature fusion short text classification model (MFFM), data information features of different layers are extracted to dynamically adjust the model features, and karst collapse disaster data are obtained or divided to improve data information calculation capacity. When geological data information parameters are analyzed and calculated according to macroscopic data information such as groundwater water level amplitude, groundwater flow velocity, groundwater chemical characteristics and groundwater turbidity, different data information is substituted into the calculation formula, and data information calculation capacity is improved.
In a specific embodiment, the alarm diagnosis module realizes karst collapse disaster information calculation through an FPGA device, and an EPF10K20TC144-4 chip is adopted by the FPGA device. In a specific embodiment, the FPGA device is structurally arranged into an array by logic function blocks, and comprises programmable logic blocks, programmable I/O modules and programmable internal connecting wires; the programmable I/O module interface is positioned on the periphery inside the FPGA device chip and consists of a logic gate, a trigger and a control unit; the programmable logic block consists of a function generator, a trigger, a data selector and a control unit; the programmable internal connecting line is positioned between the programmable logic blocks in the chip to form a connecting line network so as to improve the data information interaction and application capability.
In a specific embodiment, the karst collapse disaster early warning levels are divided into I-level early warning, II-level early warning, III-level early warning, IV-level early warning and V-level early warning;
early warning at the I level: a reminding level, wherein the possibility of disaster occurrence is very small within 24 hours, and the group survey and group prevention inspection of important geological disaster hidden danger points is started;
II-level early warning: a reminding level, wherein the possibility of disaster occurrence is low within 24 hours, and important geological disaster hidden danger points are monitored within 24 hours within forecast and early warning time;
and III-level early warning: the method comprises the following steps of (1) paying attention, starting group survey and group defense of hidden danger points of geological disasters within 24 hours, monitoring and taking defense measures within 24 hours, and reminding residents, factories, mines, schools, enterprises and public institutions near the places where disasters are easy to occur to pay close attention to weather forecast to prevent weather from suddenly deteriorating;
IV-level early warning: the method comprises the steps of (1) early warning level, wherein the probability of occurrence of a disaster is high within 24 hours, starting a temporary avoidance scheme for residents in a region threatened by a hidden danger point of a geological disaster, suspending outdoor operation near the place where the disaster easily occurs, enabling commanders on duty of each relevant unit to go to the post to prepare emergency measures, organizing emergency teams, transferring residents in dangerous zones, and paying close attention to changes of rain conditions;
and V-level early warning: an alarm level, wherein within 24 hours, the probability of disaster occurrence is high, and a temporary avoidance scheme for residents in an unstable dangerous slope threat area is started; and emergently evacuating residents, students, factories, mines, enterprises and public institutions personnel near the disaster-prone place, closing related roads and organizing personnel to prepare for emergency rescue.
In a specific embodiment, the karst collapse disaster early warning capability and the disaster early warning capability are improved by dividing the karst collapse disaster early warning level into a first-level early warning mode, a second-level early warning mode, a third-level early warning mode, a fourth-level early warning mode and a V-level early warning mode.
In a specific embodiment, the CART algorithm can be improved by combining the CART algorithm with an ensemble learning method, a plurality of classification models are selected for training, and the prediction accuracy and speed of classification problems are improved by combining respective prediction results.
In a specific embodiment, the SQL Server database is used for storing, browsing, editing, querying, outputting and modeling geological data information parameters, and the geological data information is controlled and recorded into the SQL Server database through the main control unit.
In the above embodiment, the working method of the CART alert classification module is as follows: the geological data information parameters are set as a data set D, the early warning analysis module divides the data set D into 4 categories of groundwater water level amplitude, groundwater flow velocity, groundwater water chemistry characteristics and groundwater turbidity by adopting a method of combining a CART algorithm and integrated learning, and the probability that the geological data information parameters belong to the kth category is
，/>
The output formula of the probability distribution for the kini index is:
in the formula (7), the first and second groups,
for the probability that the geological data information parameter belongs to the kth category, < >>
Is a summary ofThe output formula of the Gini index of the geological data information parameter data set D is as follows:
in the case of the formula (8),
represents the number of data belonging to the category k in the data set D, is>
The geological data information parameter dataset D is divided into 4 sub-datasets based on the characteristic a>
，/>
，/>
，/>
The output formula of the Gini index is as follows:
dividing a data set into n sub-intervals
Either of the intervals->
The output produced->
The functional formula is:
in the formula (10), the first and second groups,
is interval->
Up all->
Corresponding->
Is based on the mean value of>
For an argument parameter which causes a karst collapse disaster, is selected>
Dependent variable parameters for causing a karst cave-in disaster, wherein>
Risk loss->
The output function formula of (a) is:
in the formula (11), the first and second groups,
for an argument parameter which causes a karst collapse disaster, is selected>
Is a dependent variable parameter causing karst collapse disasters.
In the process of the specific embodiment, the CART warning classification module can be synchronously carried out with other modules, the modules are intelligently integrated in a computer or work together with the early warning analysis of karst collapse disasters, the data monitoring module acquires geological data information, the data monitoring module, the data evaluation module or the early warning analysis module, and the method can be synchronously matched with the visual display module to display early warning analysis results in a three-dimensional mode when applied.
Although specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that these specific embodiments are merely illustrative and that various omissions, substitutions and changes in the form and details of the methods and systems described above may be made by those skilled in the art without departing from the spirit and scope of the invention; for example, it is within the scope of the present invention to combine the steps of the above-described methods to perform substantially the same function in substantially the same way to achieve substantially the same result; accordingly, the scope of the invention is to be limited only by the following claims.
Claims (8)
1. A karst collapse disaster early warning analysis method is characterized by comprising the following steps: the method comprises the following steps:
acquiring geological data information through a data monitoring module, wherein the geological data information comprises underground water level information, flood collapse information, gravity collapse information, earthquake collapse information, cap soil information or opening karst parameter data information;
the data monitoring module comprises a main control unit, and a data dynamic acquisition module, an information filtering module, a data storage module and a data transmission module which are connected with the main control unit, wherein the data dynamic acquisition module is used for acquiring various geological data information causing karst collapse disasters in real time through a network geological information module WEBGIS, automatically arranging and managing the acquired geological data information in a light and heavy emergent queue mode through an active queue management algorithm model, updating the acquired geological data information in real time, controlling and managing a database SQL Server through the main control unit by the data storage module to store the geological data information, and transmitting the acquired karst collapse disaster information to other terminals in real time; the information filtering module is used for screening parameters influencing karst collapse disaster information analysis so as to improve data analysis capability; the data transmission module is used for transmitting data information;
comparing or matching the data information monitored by the data monitoring module with standard data information in a database through a data evaluation module, and realizing variable selection and parameter estimation influencing the occurrence of karst collapse disasters through a Multi-feature elastic Multi-FEA algorithm model;
and thirdly, analyzing the occurrence risk of the karst collapse disaster through an early warning analysis module, wherein the early warning analysis module comprises an early warning analysis main control module, and an alarm diagnosis module, a CART alarm classification module and an alarm output module which are connected with the early warning analysis main control module.
2. The karst collapse disaster early warning analysis method according to claim 1, characterized in that:
the method for arranging the geological data information by the active queue management algorithm model comprises the following steps:
(S1) marking underground water level information, flood collapse information, gravity collapse information, earthquake collapse information, cap soil information or opening karst parameter data information according to the cause of karst collapse disasters; dividing the early warning level of karst collapse disasters;
(S2) through 24-hour uninterrupted data information transmission, real-time monitoring of the actually acquired karst collapse disaster data volume through a queuing management change rule function, wherein the queuing management change rule function expression is as follows:
wherein ,
function expression representing a queue management change law, based on a queue management change rule>
Represents all geological data information change functions between 9>
Represents all geological data information change functions between 20>
A fluctuation function representing the change rule of karst collapse disaster data, based on the karst collapse disaster data and the karst collapse disaster data>
Represents the weight change of karst collapse disaster management data and is used for judging whether the karst collapse disaster management data is matched with the karst collapse disaster management data>
Represents the number of karst collapse disaster data information transfer batches and is used for judging whether the karst collapse disaster data information transfer batches are matched with the karst collapse disaster data information transfer batches or not>
and />
Representing the karst collapse disaster data transfer information fluctuation amount, and then judging whether the karst collapse disaster data transfer information fluctuation amount is greater than or equal to>
The information transmission times of karst collapse disaster data are represented, m represents the type of the karst collapse disaster data information, and R represents the effective rate of the karst collapse disaster data information;
and (S3) the karst collapse disaster geological data information output by the queuing management change rule function is represented by a dynamic curve, and the change trend of the karst collapse disaster management data is expressed in an image and visual mode.
3. The karst collapse disaster early warning and analysis method according to claim 1, characterized in that:
the information filtering module is provided with an ERP cloud communication module and filtering parameters connected with the ERP cloud communication module.
4. The karst collapse disaster early warning analysis method according to claim 1, characterized in that:
the method for realizing variable selection and parameter estimation of karst collapse disaster occurrence by using the Multi-feature elastic Multi-FEA algorithm model comprises the following steps:
establishing a karst collapse disaster data information multi-feature information input model to be recorded as
，/>
For N multi-characteristic geological information parameter data pairs, wherein>
For multi-characteristic information argument parameters>
，/>
For a multi-characteristic information dependent variable parameter, <' >>
And/or>
The output functional relation expression of (1) is as follows:
in the formula (2), the first and second groups,
independent variable parameter for causing karst collapse disaster>
Is influenced by>
In order to cause a karst cave-in disaster, an independent variable parameter>
For a dependent variable parameter which causes a karst collapse disaster, is selected>
Is a random disturbing term, is selected>
Normal effects due to natural geological changes; wherein->
Parameter for multiple characteristic information argument>
A matrix of weighted values of (a) is formed,
for the multi-characteristic information independent variable parameter matrix, the multi-characteristic information dependent variable parameter->
The output function of (a) is formulated as:
in the formula (3), the first and second groups of the compound,
is a target value matrix, <' > based>
Is a characteristic value matrix, is based on>
Is an estimated value matrix;
screening multiple characteristic parameters causing karst collapse disasters according to the state of the network communication system, and setting parameter threshold values causing the karst collapse disasters
Multivariate characteristic independent variable parameter causing karst collapse disaster>
And correspondingly, the multivariate characteristic independent variable parameter matrix which causes the karst collapse disaster->
Parameter thresholds considered as non-influencing variables are deleted from the parametric model to increase the selection speed, resulting in karst cave-in disasters>
The output function is formulated as:
estimation value of influence of parameter indexes on karst collapse disaster
The output function formula of (a) is:
in the formula (5), the first and second groups,
for the argument parameter matrix that causes a karst collapse disaster, <' >>
For a dependent variable parameter matrix which causes a karst collapse disaster, <' > H>
Independent variable parameter for causing karst collapse disaster>
Is used to make a karst collapse disaster total impact estimate->
The output function formula of (a) is:
in the case of the formula (6),
is the number of data pairs, is greater or less>
For an argument parameter which causes a karst collapse disaster, is selected>
For a dependent variable parameter which causes a karst collapse disaster, is selected>
For normal effects caused by natural geological changes>
The estimated value is influenced by parameter indexes causing karst collapse disasters.
5. The karst collapse disaster early warning and analysis method according to claim 1, characterized in that: the alarm diagnosis module realizes karst collapse disaster information calculation through an FPGA device, and an EPF10K20TC144-4 chip is adopted by the FPGA device.
6. The karst collapse disaster early warning analysis method according to claim 2, characterized in that: the karst collapse disaster early warning level is divided into I-level early warning, II-level early warning, III-level early warning, IV-level early warning and V-level early warning;
early warning at the I level: a reminding level, wherein the possibility of disaster occurrence is very small within 24 hours, and the group survey and group prevention inspection of important geological disaster hidden danger points is started;
and II level early warning: a reminding level, wherein the possibility of disaster occurrence is low within 24 hours, and important geological disaster hidden danger points are monitored within 24 hours within forecast and early warning time;
III-level early warning: the method comprises the following steps of (1) paying attention, starting group survey and group defense of hidden danger points of geological disasters within 24 hours, monitoring and taking defense measures within 24 hours, and reminding residents, factories, mines, schools, enterprises and public institutions near the places where disasters are easy to occur to pay close attention to weather forecast to prevent weather from suddenly deteriorating;
IV-level early warning: the early warning level is that in 24 hours, the possibility of disaster occurrence is high, a temporary avoiding scheme for residents in a region threatened by a potential hazard point of a geological disaster is started, outdoor operation near the place where the disaster easily occurs is suspended, and each relevant unit on-duty commander goes to the post to prepare emergency measures, organize emergency team, transfer residents in a dangerous zone and pay close attention to the change of rain condition;
and V-level early warning: an alarm level, wherein within 24 hours, the probability of disaster occurrence is high, and a temporary avoidance scheme for residents in an unstable dangerous slope threat area is started; and emergently evacuating residents, students, factories, mines, enterprises and public institutions personnel near the disaster-prone place, closing related roads and organizing personnel to prepare for emergency rescue.
7. The karst collapse disaster early warning analysis method according to claim 1, characterized in that: the main control unit controls and manages the database SQL Server for storing, browsing, editing, inquiring, outputting and modeling geological data information parameters, and the geological data information is controlled and input to the main control unit control and management database SQL Server through the main control unit.
8. The method of claim 1The karst collapse disaster early warning analysis method is characterized by comprising the following steps: the working method of the CART warning condition classification module comprises the following steps: the geological data information parameters are set as a data set D, the early warning analysis module divides the data set D into 4 categories of groundwater water level amplitude, groundwater flow velocity, groundwater water chemistry characteristics and groundwater turbidity by adopting a method of combining a CART algorithm and integrated learning, and the probability that the geological data information parameters belong to the kth category is
，/>
The output formula of the probability distribution for the kini index is:
in the formula (7), the first and second groups,
for the probability that the geological data information parameter belongs to the kth category>
The output formula of the Gini index of the geological data information parameter data set D is as follows:
in the formula (8), the first and second groups,
represents the number of data belonging to the category k in the data set D, is>
Dividing the geological data information parameter data set D into 4 sub-data according to the characteristics ACollecting and combining device>
，/>
，/>
，/>
The output formula of the Gini index is as follows:
dividing a data set into n sub-intervals
Either of the intervals->
The output produced->
The functional formula is:
in the formula (10), the first and second groups,
is interval->
Up all->
Corresponding->
Is based on the mean value of>
In order to cause a karst cave-in disaster, an independent variable parameter>
Parameter of dependent variable for causing karst collapse disaster, wherein>
Risk loss->
The output function of (a) is formulated as:
in the formula (11), the first and second groups,
for an argument parameter which causes a karst collapse disaster, is selected>
Is a dependent variable parameter causing karst collapse disasters. />
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