CN115965246B - Early warning analysis method for karst collapse disasters - Google Patents
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Abstract
The invention discloses a karst collapse disaster early warning analysis method, which relates to the technical field of early warning, and adopts the method that firstly, geological data information is obtained 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 the data evaluation module, and realizing variable selection and parameter estimation for influencing karst collapse disaster occurrence through a Multi-feature elastic Multi-FEA algorithm model; and thirdly, analyzing the occurrence risk of karst collapse disasters through an early warning analysis module, wherein the early warning analysis module comprises an early warning analysis main control module, and 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. The invention greatly improves the early warning analysis capability of karst collapse disasters.
Description
Technical Field
The invention relates to the technical field of information alarm, in particular to a karst collapse disaster early warning analysis method.
Background
Karst collapse (karst collapse) is a karst cave, overlying sediments and underground water, and forms a solid, liquid and gas three-phase mechanical balance system, and underground water level fluctuation reaches a certain amplitude, balance is destroyed, and overlying loose sediments suddenly collapse to form a conical collapse pit with a large upper part and a small lower part. Karst collapse is a type of geological disaster specific to karst areas, and in recent years, with rapid development of national economy construction, development of land resources, water resources and mineral resources of the karst areas is continuously enhanced, so that the problem of karst collapse caused by the development of the land resources, the water resources and the mineral resources is increasingly serious. How to develop multi-parameter monitoring and early warning to prevent and control karst collapse disaster risks is a problem to be solved urgently.
Related technical researches exist in the prior art, for example, patent number CN201921993918.5 discloses a karst collapse multi-parameter monitoring early warning test system, and the existing researches comprise a karst collapse simulation system, a groundwater parameter monitoring system and a soil body deformation monitoring system, but the real-time aspect of karst collapse disaster early warning is imperfect, the degree of automation is low, and the early warning analysis capability of the karst collapse disasters is high.
Disclosure of Invention
Aiming at the defects of the technology, the invention discloses a karst collapse disaster early warning analysis method, which can realize real-time acquisition and analysis of geological data information, adopts a network geological information module WEBGIS and a hypertext transmission HTTP real-time transmission module to realize real-time acquisition and transmission of the geological data information, adopts a Multi-feature elastic algorithm Multi-FEA to construct the data model to ensure the accuracy and speed of early warning analysis, and greatly improves the real-time performance of early warning while ensuring the early warning accuracy.
In order to solve the technical problems, the invention adopts the following technical scheme:
the early warning analysis method of karst collapse disasters comprises the following steps:
firstly, obtaining geological data information through a data monitoring module, wherein the geological data information comprises groundwater level information, flood induced collapse information, gravity induced collapse information, earthquake induced collapse information, overburden soil information or open karst parameter data information;
the data monitoring module comprises a main control unit, a data dynamic acquisition module, an information filtering module, a data storage module and a data transmission module, wherein the data dynamic acquisition module is connected with the main control unit, 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-heavy urgent queue mode through an active queue management algorithm model, updating the acquired geological data information in real time, the data storage module is used for controlling and managing a database SQL Server to store the geological data information through the main control unit, and the data transmission module is used for transmitting the acquired karst collapse disaster information to other terminals in real time; the information filtering module is used for screening parameters affecting 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 the data evaluation module, and realizing variable selection and parameter estimation for influencing karst collapse disaster occurrence through a Multi-feature elastic Multi-FEA algorithm model;
and thirdly, analyzing the occurrence risk of karst collapse disasters through an early warning analysis module, wherein the early warning analysis module comprises an early warning analysis main control module, and 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.
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 groundwater level information, flood induced collapse information, gravity induced collapse information, earthquake induced collapse information, overburden soil information or open karst parameter data information according to the cause of karst collapse disaster; dividing karst collapse disaster early warning grades;
(S2) through 24-hour uninterrupted data information transmission, the actual acquired karst collapse disaster data volume is monitored in real time through a queuing management change rule function, and the expression of the queuing management change rule function is as follows:
wherein ,
express queuing management change rule function expression +.>
Representing all geological data information change functions between 9:00-20:00,/for each geological data information change function>
Representing all geological data information change functions between 20:00-8:00,/for each geological data information change function>
Representing karst collapse disaster data change law fluctuation function, < ->
Weight change of karst collapse disaster management data is represented, < ->
Indicating the number of karst collapse disaster data information transfer batches,/-for>
and />
Representing fluctuation quantity of karst collapse disaster data transmission information under different karst collapse disaster data information transmission batches, < +.>
The karst collapse disaster data information transmission times are represented, m represents the karst collapse disaster data information types, and R represents the effective rate of the karst collapse disaster data information;
and S3, displaying the karst collapse disaster geological data information output by the queuing management change rule function through a dynamic curve, and displaying the karst collapse disaster management data change trend in a visual 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 the Multi-feature elastic Multi-FEA algorithm model comprises the following steps:
establishing a karst collapse disaster data information multi-element characteristic information input model, and recording the model as
，/>
For N multi-characteristic geological information parameter data pairs, wherein +.>
Is a multi-characteristic information argument parameter, +.>
，/>
Is a multi-characteristic information dependent variable parameter, +.>
And->
The output functional relation expression of (2) is:
in the formula (2) of the present invention,
independent variable parameter for causing karst collapse disaster>
Degree of influence of->
Independent variable parameters for karst collapse disaster, < +.>
Dependent variable parameters for causing karst collapse disaster, < +.>
Is a random interference item->
Normal effects caused by natural geological changes; wherein->
Independent variable parameter for multi-characteristic information>
Weight matrix of>
For the multi-characteristic information independent variable parameter matrix, the multi-characteristic information independent variable parameter is +.>
The output function formula of (2) is:
in the formula (3) of the present invention,
for the target value matrix +.>
Is a characteristic value matrix>
Is an estimated value matrix;
screening the multiple characteristic parameters causing the karst collapse disaster according to the state of the network communication system, and setting a parameter threshold value causing the karst collapse disaster
Multicomponent characteristic independent variable parameter for karst collapse disaster +.>
When in use, the corresponding multi-feature independent variable parameter matrix causing karst collapse disasters is +.>
The parameter threshold value which is regarded as the influence-free variable is deleted from the parameter model to improve the selection speed and cause karst collapse disaster +.>
The output function formula is:
parameter index influence estimated value for causing karst collapse disaster
The output function formula of (2) is:
in the formula (5) of the present invention,
an independent variable parameter matrix for causing karst collapse disasters,>
dependent variable parameter matrix for causing karst collapse disaster, < ->
Independent variable parameter for causing karst collapse disaster>
Is an influence degree matrix of (1) causing karst collapse disaster total influence estimation +.>
The output function formula of (2) is:
in the formula (6) of the present invention,
for the number of data pairs>
Independent variable parameters for karst collapse disaster, < +.>
Dependent variable parameters for causing karst collapse disaster, < +.>
Is a normal effect caused by natural geological changes, +.>
The estimated value is influenced by the parameter index for causing karst collapse disasters.
As a further technical scheme of the invention, the warning condition diagnosis module realizes karst collapse disaster information calculation through an FPGA device, and the FPGA device chip adopts EPF10K20TC144-4.
As a further technical scheme of the invention, karst collapse disaster warning grades are divided into grade I warning, grade II warning, grade III warning, grade IV warning and grade V warning;
i-stage early warning: reminding, namely, the disaster occurrence possibility is very small within 24 hours, and the crowd-monitoring crowd-proofing inspection of important geological disaster hidden danger points is started;
II, early warning: the reminding level is low in disaster occurrence probability within 24 hours, and important geological disaster hidden danger points are monitored within the forecasting and early-warning time for 24 hours;
III level early warning: the attention level is that the disaster occurrence possibility is high within 24 hours, the geological disaster hidden danger point group measurement and prevention is started within the forecast and early warning time, and the defending measures are adopted by 24 hours monitoring, so that residents, factories, mines, schools, enterprises and public institutions nearby the disaster prone place are reminded of paying close attention to weather forecast, and sudden weather deterioration is prevented;
IV-level early warning: early warning stage, within 24 hours, the disaster occurrence probability is high, a temporary avoidance scheme of residents in a region threatened by geological disaster hidden danger is started, outdoor operation near the disaster easy place is suspended, each relevant single on duty commander arrives at the post to prepare emergency measures, rescue teams are organized, residents in dangerous areas are transferred, and the change of rain conditions is closely noticed;
v-level early warning: alarm level, within 24 hours, the disaster occurrence possibility is high, and a temporary avoidance scheme of residents in an unstable dangerous slope threat area is started; residents, students, factories and mines, enterprises and public institutions personnel nearby the location where the emergency evacuation disaster is easy to occur close related roads, and organisers 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 by the main control unit to be input into the SQL Server database.
As a further technical scheme of the invention, the working method of the CART alarm classification module is as follows: the geological data information parameter is set as a data set D, the data set D is divided into 4 categories of groundwater level amplitude, groundwater flow speed, groundwater chemical characteristics and groundwater turbidity by adopting a CART algorithm and integrated learning combination method by the early warning analysis module, and the probability that the geological data information parameter belongs to the kth category is that
，/>
The output formula of the base-Ni index of the probability distribution is:
in the formula (7) of the present invention,
probability of belonging to the kth category for the geological data information parameter,/for the geological data information parameter>
And the output formula of the matrix index of the geological data information parameter data set D is as follows:
in the formula (8) of the present invention,
representing the number of data belonging to category k in data set D,/for each data set>
Dividing the geological data information parameter data set D into 4 sub-data sets according to the characteristic A>
，/>
，/>
，/>
The base-index output formula of (c) is:
dividing a data set into n subintervals
Any one interval +.>
The output produced->
The function formula is:
in the formula (10) of the present invention,
for interval->
Go up all->
Corresponding->
Mean value of->
Independent variable parameters for karst collapse disaster, < +.>
Dependent variable parameters for causing karst collapse disasters, wherein +.>
Risk loss->
The output function formula of (2) is:
in the formula (11) of the present invention,
independent variable parameters for karst collapse disaster, < +.>
Is a dependent variable parameter for causing karst collapse disasters.
The invention has positive beneficial effects different from the prior art: according to the invention, geological data information is obtained through the data monitoring module, the data information monitored by the data monitoring module is compared or matched with standard data information in a database through the data evaluation module, and variable selection and parameter estimation for influencing karst collapse disaster are realized through the 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, and 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, so that real-time acquisition and analysis of geological data information can be realized, real-time acquisition and transmission of the geological data information are realized by adopting a network geological information module WEBGIS and HTTP real-time transmission technology, the accuracy and the speed of early warning analysis are ensured by adopting a Multi-feature elastic algorithm Multi-FEA to construct the data model, and the real-time performance of early warning is greatly improved while the early warning accuracy is ensured.
Drawings
For a clearer description of embodiments of the invention or of solutions in the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are only some embodiments of the invention, from which, without inventive faculty, other drawings can be obtained for a person skilled in the art, in which:
FIG. 1 is a schematic diagram of the overall architecture of a karst collapse disaster early warning analysis method;
FIG. 2 is a schematic structural diagram of a data monitoring module in a karst collapse disaster early warning analysis method according to the present invention;
FIG. 3 is a schematic diagram of a model principle and architecture of a Multi-feature elastic algorithm Multi-FEA in a karst collapse disaster early warning analysis method;
fig. 4 is a schematic diagram of a model principle architecture of a CART algorithm and an integrated learning method in the early warning analysis method of karst collapse disasters.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
1-4, the early warning analysis method for karst collapse disasters comprises the following steps:
firstly, obtaining geological data information through a data monitoring module, wherein the geological data information comprises groundwater level information, flood induced collapse information, gravity induced collapse information, earthquake induced collapse information, overburden soil information or open karst parameter data information; in specific embodiments, the method is not limited to the above method, and further includes various data information for initiating karst collapse disasters;
the data monitoring module comprises a main control unit, a data dynamic acquisition module, an information filtering module, a data storage module and a data transmission module, wherein the data dynamic acquisition module is connected with the main control unit, 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-heavy urgent queue mode through an active queue management algorithm model, updating the acquired geological data information in real time, the data storage module is used for controlling and managing a database SQL Server to store the geological data information through the main control unit, and the data transmission module is used for transmitting the acquired karst collapse disaster information to other terminals in real time; the information filtering module is used for screening parameters affecting 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 the data evaluation module, and realizing variable selection and parameter estimation for influencing karst collapse disaster occurrence through a Multi-feature elastic Multi-FEA algorithm model; the variable selection and the parameter estimation can be the change of external environmental factors such as weather variables, environmental variables and the like in the specific embodiment, and the parameter estimation is also parameter information influencing the prediction of karst collapse disasters;
and thirdly, analyzing the occurrence risk of karst collapse disasters through an early warning analysis module, wherein the early warning analysis module comprises an early warning analysis main control module, and 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.
In the above embodiment, the network geological information module WEBGIS implements real-time collection, management and storage of geological data information through an internal and external embedded hypertext transfer HTTP standard application system, and tracks karst collapse displacement and direction in real time in combination with a network deployment system so as to improve early warning speed of a karst collapse disaster, and the working tasks of the hypertext transfer HTTP real-time transfer module include synchronous refreshing of a client timer, asynchronous transfer based on AJAX and Push of a server.
In a specific embodiment, a general gateway interface development technology in a network geological information module WEBGIS is selected to guide user information into a data monitoring system, and corresponding internal operation check data are matched, system monitoring and management and control operation is performed on the premise of guaranteeing complete collection of landslide data, redundancy of the internal landslide data is reduced, and simultaneously displacement and direction of a landslide are tracked by combining a central allocation system with stronger network connectivity to achieve timely updating of geological states.
In the above embodiment, the SQL Server database is used for storing, browsing, editing, querying, outputting and modeling parameters of geological data information, and the geological data information is controlled by the main control unit to be input into the SQL Server database.
In particular embodiments, the SQL Server database is an extensible, high-performance database management system designed for distributed client/Server computing, implementing an organic combination with Window NT.
In the above embodiment, the data monitoring module may also be a collection mode such as satellite data information transmission and high-altitude remote sensing.
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 groundwater level information, flood induced collapse information, gravity induced collapse information, earthquake induced collapse information, overburden soil information or open karst parameter data information according to the cause of karst collapse disaster; dividing karst collapse disaster early warning grades;
in this step, since the data information for causing the karst collapse disaster is many, the specific embodiment is not limited to the above embodiment, and various data information in the above embodiment can be listed separately, or the data information with relatively large influence degree can be identified by different data information, so as to improve the recognition degree and the capability of different data information;
(S2) through 24-hour uninterrupted data information transmission, the actual acquired karst collapse disaster data volume is monitored in real time through a queuing management change rule function, and the expression of the queuing management change rule function is as follows:
wherein ,
express queuing management change rule function expression +.>
Representing all geological data information change functions between 9:00-20:00,/for each geological data information change function>
Representing all geological data information change functions between 20:00-8:00,/for each geological data information change function>
Representing karst collapse disaster data change law fluctuation function, < ->
Weight change of karst collapse disaster management data is represented, < ->
Indicating the number of karst collapse disaster data information transfer batches,/-for>
and />
Representing fluctuation quantity of karst collapse disaster data transmission information under different karst collapse disaster data information transmission batches, < +.>
The number of times of transmitting karst collapse disaster data information is represented, m represents the type of the karst collapse disaster data information, and R represents the existence of the karst collapse disaster data informationEfficiency is improved; />
In the step, the queuing management change rule function expression can reflect factors of lava collapse data information to a certain extent, and the data information is effectively expressed through a macroscopic data function, so that the 24-hour uninterrupted data information monitoring capability is improved. In order to improve the data computing capability, the functions are respectively divided into different functions to be represented, wherein all geological data information change functions between 9:00 and 20:00, such as the time of day and night, are easily affected by different data information such as temperature, air temperature, illumination and the like, and the parameters are considered by the geological data information change functions, and in a specific embodiment, the parameters are not limited to the above factors. All geological data information changing functions between 9:00 and 20:00 are also similar to the above explanation and description, and should represent other factors affecting the change of geological data information, such as different data information of geographic conditions, magnetic fields, location information, etc. The karst collapse disaster management data weight change indicates that when different data information is measured, measurement errors are easily caused by detecting the difference of various information such as positions, spaces, structures and the like each time. Because the data information of karst collapse disasters induces many factors, in the specific embodiments, the specific embodiments are not necessarily all contained, and the invention gives out key factors;
and S3, displaying the karst collapse disaster geological data information output by the queuing management change rule function through a dynamic curve, and displaying the karst collapse disaster management data change trend in a visual and visual mode.
In a specific embodiment, when different parameter data information is expressed through microscopic data information, the macroscopic data information is converted into microscopic data information for analysis, so that 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 filtering parameters connected with the ERP cloud communication module. For example, in order to improve the computing capacity of data information, 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 application and computing capacity of the data information.
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 is as follows:
establishing a karst collapse disaster data information multi-element characteristic information input model, and recording the model as
，/>
For N multi-characteristic geological information parameter data pairs, wherein +.>
Is a multi-characteristic information argument parameter, +.>
，/>
Is a multi-characteristic information dependent variable parameter, +.>
And->
The output functional relation expression of (2) is:
in the formula (2) of the present invention,
independent variable parameter for causing karst collapse disaster>
Degree of influence of->
Independent variable parameters for karst collapse disaster, < +.>
Dependent variable parameters for causing karst collapse disaster, < +.>
Is a random interference item->
Normal effects caused by natural geological changes; wherein->
Independent variable parameter for multi-characteristic information>
Weight matrix of>
For the multi-characteristic information independent variable parameter matrix, the multi-characteristic information independent variable parameter
The output function formula of (2) is:
in the formula (3) of the present invention,
for the target value matrix +.>
Is a characteristic value matrix>
Is an estimated value matrix;
screening the multiple characteristic parameters causing the karst collapse disaster according to the state of the network communication system, and setting a parameter threshold value causing the karst collapse disaster
Multicomponent characteristic independent variable parameter for karst collapse disaster +.>
When in use, the corresponding multi-feature independent variable parameter matrix causing karst collapse disasters is +.>
The parameter threshold value which is regarded as the influence-free variable is deleted from the parameter model to improve the selection speed and cause karst collapse disaster +.>
The output function formula is:
parameter index influence estimated value for causing karst collapse disaster
The output function formula of (2) is:
in the formula (5) of the present invention,
an independent variable parameter matrix for causing karst collapse disasters,>
dependent variable parameter matrix for causing karst collapse disaster, < ->
Independent variable parameter for causing karst collapse disaster>
Is an influence degree matrix of (1) causing karst collapse disaster total influence estimation +.>
The output function formula of (2) is:
in the formula (6) of the present invention,
for the number of data pairs>
Independent variable parameters for karst collapse disaster, < +.>
Dependent variable parameters for causing karst collapse disaster, < +.>
Is a normal effect caused by natural geological changes, +.>
The estimated value is influenced by the parameter index for 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, the function realizes variable selection and parameter estimation of karst collapse disaster occurrence through a Multi-feature fused short text classification model (Multi-FEA-ture fusion model, MFFM), and data information features of different layers are extracted to dynamically adjust the model features, and karst collapse disaster data are acquired or divided according to the obtained or divided data information calculation capacity. When the geological data information parameters are analyzed and calculated according to macroscopic data information such as groundwater level amplitude, groundwater flow speed, groundwater chemical characteristics, groundwater turbidity and the like, the data information calculation capability is improved by bringing different data information into the calculation formula.
In a specific embodiment, the warning condition diagnosis module realizes karst collapse disaster information calculation through an FPGA device, and the FPGA device chip adopts EPF10K20TC144-4. In a specific embodiment, the FPGA device is structurally arranged as an array from logic function blocks, and the FPGA device includes programmable logic blocks, programmable I/O modules, and programmable interconnects; the programmable I/O module interface is positioned around the inside of 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 lines are positioned between the programmable logic blocks in the chip to form a connecting network so as to improve the data information interaction and application capability.
In a specific embodiment, karst collapse disaster warning grades are classified into grade I warning, grade II warning, grade III warning, grade IV warning and grade V warning;
i-stage early warning: reminding, namely, the disaster occurrence possibility is very small within 24 hours, and the crowd-monitoring crowd-proofing inspection of important geological disaster hidden danger points is started;
II, early warning: the reminding level is low in disaster occurrence probability within 24 hours, and important geological disaster hidden danger points are monitored within the forecasting and early-warning time for 24 hours;
III level early warning: the attention level is that the disaster occurrence possibility is high within 24 hours, the geological disaster hidden danger point group measurement and prevention is started within the forecast and early warning time, and the defending measures are adopted by 24 hours monitoring, so that residents, factories, mines, schools, enterprises and public institutions nearby the disaster prone place are reminded of paying close attention to weather forecast, and sudden weather deterioration is prevented;
IV-level early warning: early warning stage, within 24 hours, the disaster occurrence probability is high, a temporary avoidance scheme of residents in a region threatened by geological disaster hidden danger is started, outdoor operation near the disaster easy place is suspended, each relevant single on duty commander arrives at the post to prepare emergency measures, rescue teams are organized, residents in dangerous areas are transferred, and the change of rain conditions is closely noticed;
v-level early warning: alarm level, within 24 hours, the disaster occurrence possibility is high, and a temporary avoidance scheme of residents in an unstable dangerous slope threat area is started; residents, students, factories and mines, enterprises and public institutions personnel nearby the location where the emergency evacuation disaster is easy to occur close related roads, and organisers prepare for emergency rescue.
In the 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 the forms of I-level early warning, II-level early warning, III-level early warning, IV-level early warning and V-level early warning.
In a specific embodiment, the CART algorithm can be improved by combining the CART algorithm with the integrated learning method, a plurality of classification model training is selected, and the prediction results of the classification model training are combined to improve the prediction accuracy and speed of the classification problem.
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 by the main control unit to be input into the SQL Server database.
In the above embodiment, the working method of the CART alarm classification module is as follows: the geological data information parameter is set as a data set D, the data set D is divided into 4 categories of groundwater level amplitude, groundwater flow speed, groundwater chemical characteristics and groundwater turbidity by adopting a CART algorithm and integrated learning combination method by the early warning analysis module, and the probability that the geological data information parameter belongs to the kth category is that
，/>
The output formula of the base-Ni index of the probability distribution is:
in the formula (7) of the present invention,
probability of belonging to the kth category for the geological data information parameter,/for the geological data information parameter>
Is a probability distributionThe matrix index output formula of the geological data information parameter data set D is as follows:
in the formula (8) of the present invention,
representing the number of data belonging to category k in data set D,/for each data set>
Dividing the geological data information parameter data set D into 4 sub-data sets according to the characteristic A>
，/>
，/>
，/>
The base-index output formula of (c) is:
dividing a data set into n subintervals
Any one interval +.>
The output produced->
The function formula is:
in the formula (10) of the present invention,
for interval->
Go up all->
Corresponding->
Mean value of->
Independent variable parameters for karst collapse disaster, < +.>
Dependent variable parameters for causing karst collapse disasters, wherein +.>
Risk loss->
The output function formula of (2) is:
in the formula (11) of the present invention,
independent variable parameters for karst collapse disaster, < +.>
Is a dependent variable parameter for causing karst collapse disasters.
In the process of the specific embodiment, the CART alarm classification module can be synchronously carried out with the other modules, the modules are intelligently integrated in a computer or work together with early warning analysis of karst collapse disasters, acquisition of geological data information by 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 when applied so as to display the early warning analysis result in a three-dimensional mode.
While specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that the foregoing detailed description is given by way of example only, and that various omissions, substitutions and changes in the form of the details of the method and system illustrated 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 above-described method steps to perform substantially the same function in substantially the same way to achieve substantially the same result; accordingly, the scope of the invention is limited only by the following claims.
Claims (7)
1. A karst collapse disaster early warning analysis method is characterized in that: the method comprises the following steps:
firstly, obtaining geological data information through a data monitoring module, wherein the geological data information comprises groundwater level information, flood induced collapse information, gravity induced collapse information, earthquake induced collapse information, overburden soil information or open karst parameter data information;
the data monitoring module comprises a main control unit, a data dynamic acquisition module, an information filtering module, a data storage module and a data transmission module, wherein the data dynamic acquisition module is connected with the main control unit, 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-heavy urgent queue mode through an active queue management algorithm model, updating the acquired geological data information in real time, the data storage module is used for controlling and managing a database SQL Server to store the geological data information through the main control unit, and the data transmission module is used for transmitting the acquired karst collapse disaster information to other terminals in real time; the information filtering module is used for screening parameters affecting 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 the data evaluation module, and realizing variable selection and parameter estimation for influencing karst collapse disaster occurrence through a Multi-feature elastic Multi-FEA algorithm model;
analyzing karst collapse disaster occurrence risks through an early warning analysis module, wherein the early warning analysis module comprises an early warning analysis main control module, and 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;
the method for realizing variable selection and parameter estimation of karst collapse disaster occurrence by the Multi-feature elastic Multi-FEA algorithm model comprises the following steps:
establishing a karst collapse disaster data information multi-element characteristic information input model, and recording the model as
，/>
For N multi-characteristic geological information parameter data pairs, wherein +.>
Is a multi-characteristic information argument parameter, +.>
，/>
Is a multi-characteristic information dependent variable parameter, +.>
And->
The output functional relation expression of (2) is:
in the case of the formula (1),
independent variable parameter for causing karst collapse disaster>
Degree of influence of->
Independent variable parameters for karst collapse disaster, < +.>
Dependent variable parameters for causing karst collapse disaster, < +.>
Is a random interference item->
Normal effects caused by natural geological changes; wherein->
Independent variable parameter for multi-characteristic information>
Weight matrix of>
For the multi-characteristic information independent variable parameter matrix, the multi-characteristic information independent variable parameter is +.>
The output function formula of (2) is:
in the formula (2) of the present invention,
for the target value matrix +.>
Is a characteristic value matrix>
Is an estimated value matrix;
screening the multiple characteristic parameters causing the karst collapse disaster according to the state of the network communication system, and setting a parameter threshold value causing the karst collapse disaster
Multicomponent characteristic independent variable parameter for karst collapse disaster +.>
When in use, the corresponding multi-feature independent variable parameter matrix causing karst collapse disasters is +.>
The parameter threshold value which is regarded as the influence-free variable is deleted from the parameter model to improve the selection speed and cause karst collapse disaster +.>
The output function formula is:
parameter index influence estimated value for causing karst collapse disaster
The output function formula of (2) is:
in the formula (4) of the present invention,
an independent variable parameter matrix for causing karst collapse disasters,>
dependent variable parameter matrix for causing karst collapse disaster, < ->
Independent variable parameter for causing karst collapse disaster>
Is an influence degree matrix of (1) causing karst collapse disaster total influence estimation +.>
The output function formula of (2) is:
in the formula (5) of the present invention,
for the number of data pairs>
Independent variable parameters for karst collapse disaster, < +.>
Dependent variable parameters for causing karst collapse disaster, < +.>
Is a normal effect caused by natural geological changes, +.>
The estimated value is influenced by the parameter index for causing karst collapse disasters.
2. The method for early warning analysis of karst collapse disasters according to claim 1, wherein the method comprises the following steps:
the method for arranging the geological data information by the active queue management algorithm model comprises the following steps:
(S1) marking groundwater level information, flood induced collapse information, gravity induced collapse information, earthquake induced collapse information, overburden soil information or open karst parameter data information according to the cause of karst collapse disaster; dividing karst collapse disaster early warning grades;
(S2) through 24-hour uninterrupted data information transmission, the actual acquired karst collapse disaster data volume is monitored in real time through a queuing management change rule function, and the expression of the queuing management change rule function is as follows:
wherein ,
express queuing management change rule function expression +.>
Representing all geological data information change functions between 9:00-20:00,/for each geological data information change function>
Representing all geological data information change functions between 20:00-8:00,/for each geological data information change function>
Representing karst collapse disaster data change law fluctuation function, < ->
Weight change of karst collapse disaster management data is represented, < ->
Indicating the number of karst collapse disaster data information transfer batches,/-for>
and />
Representing fluctuation quantity of karst collapse disaster data transmission information under different karst collapse disaster data information transmission batches, < +.>
The karst collapse disaster data information transmission times are represented, m represents the karst collapse disaster data information types, and R represents the effective rate of the karst collapse disaster data information;
(S3) the karst collapse disaster geological data information output by the queuing management change rule function is expressed through a dynamic curve, and the karst collapse disaster management data change trend is expressed in a visual and visual mode;
and S3, displaying the karst collapse disaster geological data information output by the queuing management change rule function through a dynamic curve, and displaying the karst collapse disaster management data change trend in a visual and visual mode.
3. The method for early warning analysis of karst collapse disasters according to claim 1, wherein the method comprises the following steps:
the information filtering module is provided with an ERP cloud communication module and filtering parameters connected with the ERP cloud communication module.
4. The method for early warning analysis of karst collapse disasters according to claim 1, wherein the method comprises the following steps: the warning condition diagnosis module realizes karst collapse disaster information calculation through an FPGA device, and the FPGA device chip adopts EPF10K20TC144-4.
5. The method for early warning analysis of karst collapse disasters according to claim 2, wherein the method comprises the following steps: the karst collapse disaster early warning grades are divided into grade I early warning, grade II early warning, grade III early warning, grade IV early warning and grade V early warning;
i-stage early warning: reminding, namely, the disaster occurrence possibility is very small within 24 hours, and the crowd-monitoring crowd-proofing inspection of important geological disaster hidden danger points is started;
II, early warning: the reminding level is low in disaster occurrence probability within 24 hours, and important geological disaster hidden danger points are monitored within the forecasting and early-warning time for 24 hours;
III level early warning: the attention level is that the disaster occurrence possibility is high within 24 hours, the geological disaster hidden danger point group measurement and prevention is started within the forecast and early warning time, and the defending measures are adopted by 24 hours monitoring, so that residents, factories, mines, schools, enterprises and public institutions nearby the disaster prone place are reminded of paying close attention to weather forecast, and sudden weather deterioration is prevented;
IV-level early warning: early warning stage, within 24 hours, the disaster occurrence probability is high, a temporary avoidance scheme of residents in a region threatened by geological disaster hidden danger is started, outdoor operation near the disaster easy place is suspended, each relevant single on duty commander arrives at the post to prepare emergency measures, rescue teams are organized, residents in dangerous areas are transferred, and the change of rain conditions is closely noticed;
v-level early warning: alarm level, within 24 hours, the disaster occurrence possibility is high, and a temporary avoidance scheme of residents in an unstable dangerous slope threat area is started; residents, students, factories and mines, enterprises and public institutions personnel nearby the location where the emergency evacuation disaster is easy to occur close related roads, and organisers prepare for emergency rescue.
6. The method for early warning analysis of karst collapse disasters according to claim 1, wherein the method comprises the following steps: the main control unit controls the management database SQL Server to store, browse, edit, inquire, output and model the geological data information parameters, and the geological data information is input to the main control unit through the main control unit.
7. The method for early warning analysis of karst collapse disasters according to claim 1, wherein the method comprises the following steps: the working method of the CART alarm classification module is as follows: the geological data information parameter is set as a data set D, the data set D is divided into 4 categories of groundwater level amplitude, groundwater flow speed, groundwater chemical characteristics and groundwater turbidity by adopting a CART algorithm and integrated learning combination method by the early warning analysis module, and the probability that the geological data information parameter belongs to the kth category is that
，/>
The output formula of the base-Ni index of the probability distribution is:
in the formula (7) of the present invention,
probability of belonging to the kth category for the geological data information parameter,/for the geological data information parameter>
And the output formula of the matrix index of the geological data information parameter data set D is as follows:
in the formula (8) of the present invention,
representing the number of data belonging to category k in data set D,/for each data set>
Dividing the geological data information parameter data set D according to the characteristic ADivided into 4 sub-data sets->
，/>
，/>
，/>
The base-index output formula of (c) is:
dividing a data set into n subintervals
Any one interval +.>
The output produced->
The function formula is:
in the formula (10) of the present invention,
for interval->
Go up all->
Corresponding->
Mean value of->
Independent variable parameters for karst collapse disaster, < +.>
Dependent variable parameters for causing karst collapse disasters, wherein +.>
Risk loss->
The output function formula of (2) is:
in the formula (11) of the present invention,
independent variable parameters for karst collapse disaster, < +.>
Is a dependent variable parameter for causing karst collapse disasters. />
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