CN112946240A - Landslide geological disaster gene identification and prediction system - Google Patents
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Abstract
The invention relates to a landslide geological disaster gene identification and prediction system, which comprises a side slope big data unit, a characteristic data analysis unit and a landslide identification and prediction unit; the system judges, identifies and predicts the safety and stability of the side slope in real time through the big data of the side slope, and judges, evaluates and predicts the side slope slide according to the real-time data and historical data of the sky-ground-human in the side slope area without quantitative calculation and analysis; on the other hand, disaster-causing gene repair can be performed by judging and selecting slope disease genes, and landslide disasters can be prevented and treated. Meanwhile, the method overcomes the limitation of the traditional landslide prediction method in processing big data problems. After networking with meteorological satellites and the like and positioning by a Beidou system, landslide geological disaster prediction and forecast can be realized, and the method is simple to operate, high in data mass, automation, intelligent degree and identification precision.
Description
Technical Field
The invention relates to a landslide geological disaster gene identification and prediction system, in particular to an advanced identification and prediction technology for potential landslide geological disasters of roads, mines and natural side slopes. On one hand, the method can be used for safety stability evaluation of various slopes and prediction of potential landslides of the slopes under various inducing conditions; on the other hand, the method can be used for preventing and treating side slopes and pre-warning landslides. The method is used for researching the safety stability of the side slope under complex influence conditions and the response characteristics of the side slope under the disturbance of rainfall, ice and snow, melting and swelling, earthquake, human engineering activities and the like. Belongs to the field of geological engineering and geotechnical engineering.
Background
Landslide is a common geological disaster, has the characteristics of frequent occurrence, multiple occurrence, high risk and the like, and has very important significance for evaluating and preventing disasters by accurately and timely judging and predicting landslide geological disasters. However, the identification and prediction of landslide disasters is a difficult problem. The landslide disaster-pregnancy environment is extremely complex, influence factors are various, at present, the judgment and prediction of disaster risks are mainly based on detection/monitoring, statistics, disaster mechanisms, simulation, nonlinearity and other methods, and the methods have certain reliability on limited time sequence data, qualitative and disaster-impending early warning. However, landslide is closely related to slope conditions, geological environment, meteorological conditions and human activities, and is a complex day-ground-human full time-space and universe big data problem, and the traditional method meets technical bottlenecks which are difficult to overcome for quantitative and accurate judgment and prediction of complex and variable holographic mass data.
In a classical landslide prediction method based on a physical model, accurate rock-soil body parameters need to be relied on. The existing parameter acquisition method mainly depends on indoor tests, is difficult to accurately acquire rock-soil body in-situ parameters, has quick environmental change and has great uncertainty and randomness. Therefore, the traditional research method is difficult to solve the problem of accurate judgment, identification and prediction of the landslide disaster under the complex environmental condition. In the face of complex conditions, the traditional physical mechanics analysis method, numerical simulation, non-linearity and other methods are still difficult to process the problems of isomerism, diversification and the like of data, the limitation of the existing method needs to be broken through, and an subversive thought is adopted to provide an effective solution for identifying, predicting and preventing and controlling landslide disaster risks under complex environmental conditions.
Research and extensive practice have shown that landslides are associated with slope geometry, attitude, topographic geology, geotechnical properties, regional geological structures, meteorological conditions, and human activities. Whether the slope is landslide or not depends on the intrinsic endowment of the slope, the slope has the characteristic of easy skidding such as a soft structural surface, a soft rock mass and high and steep slope, and the slope is subjected to landslide under the disturbance of external factors such as vibration, rainfall, earthquake, freeze thawing and the like. That is, when the slope itself has some slip-causing factor or under disturbance of an external factor, the slope will slide. It can be seen that whether the side slope is landslide or not is related to the characteristics of the 'gene' of the side slope, and when the 'disaster-causing gene' of the side slope exists or the side slope is subjected to 'gene mutation' under the influence of external environmental factors to generate the disaster-causing gene, the side slope will be landslide.
Thus, landslide has obvious genetic characteristics. By researching the gene characteristics of the landslide, a scientific method can be provided for accurate identification, prediction and evaluation of the landslide. With the fusion of information technologies such as big data, machine learning, artificial intelligence and the like, the disaster sensing and predicting technology provides a more scientific, accurate and efficient technical method for disaster risk identification and prediction.
The invention establishes a landslide geological disaster gene identification and prediction method, which can not only carry out risk evaluation on the stability and safety of different types of side slopes according to the characteristics of the side slopes, geological environment and meteorological conditions, but also identify and predict the potential landslide possibility of the side slopes under the conditions.
Disclosure of Invention
The invention aims to establish a landslide geological disaster gene identification and prediction system, the invention can find disaster-causing genes in time by carrying out early gene identification on the side slope, and the identification, evaluation and prediction are carried out on the side slope landslide according to the sky-ground-human big data of the side slope region under the condition of not needing quantitative calculation; on the other hand, disaster-causing gene repair can be performed by judging and selecting slope disease genes, and landslide disasters can be prevented and treated. Meanwhile, the method overcomes the limitation of the traditional landslide prediction method in processing big data problems.
The landslide geological disaster gene identification and prediction system comprises a side slope big data unit, a characteristic data analysis unit and a landslide identification and prediction unit.
The slope big data unit is a system for extracting, preprocessing and converting sky-ground-person data influencing landslide, and the unit acquires, extracts, inputs and preprocesses slope position, meteorological data, topographic geology, regional geological structure, slope structure and artificial disturbance data and provides original data for genetic identification and prediction of landslide. Wherein:
the slope position mainly comes from a satellite positioning system, and the main parameters comprise longitude and latitude, three-dimensional coordinates, an azimuth angle and a direction angle of the geographical position of the slope, and the slope position is provided by a Beidou satellite positioning system by adopting a yellow sea coordinate system;
the weather data mainly come from a weather satellite and are dynamic real-time data, the weather data comprise changes of wind, frost, rain and snow, and the parameters mainly comprise atmospheric temperature, atmospheric humidity, rainfall intensity, snow thickness and frozen soil depth;
the topographic and geological data mainly come from resource environment satellites and are corrected by topographic and geological survey data and geotechnical engineering survey data of the slope field, and the parameters mainly comprise the type and the gradient of the slope, the lithology of the slope stratum, the type and the thickness of a rock body, underground water and the inclination angle of the basement stratum;
the regional geological structure data mainly come from a seismic table network and regional geological survey, and the parameters mainly comprise faults, fault distribution, fault grades and the relation between the fault grades and slopes, seismic magnitude and seismic intensity;
the side slope structure data mainly come from resource environment satellites, side slope design and side slope geological survey data, and the parameters mainly comprise the height, the gradient and the inclination of a side slope and steps;
the human activity data mainly come from a resource environment satellite, and the parameters mainly comprise the load size and strength, slope toe excavation, blasting, vehicle running and the like;
the characteristic data analysis unit consists of a conversion box, an amplifier, a digital strain gauge and a data integration box; the analog signals are converted into digital signals through a conversion box and then enter a Data Integration Box (DIB) through the digital strain gauge, the vibration signals are amplified through the amplifier and then enter the data integration box through the digital strain gauge, and the temperature, humidity, terrain and slope information directly enters the data integration box to be subjected to data analysis and integration and then is uploaded to the landslide identification and prediction unit;
the characteristic data analysis is a gene characteristic library established after the landslide gene attribute is characterized by the landslide gene characteristic extracted from big data influencing landslide through the existing research results and literature analysis and gene coding is carried out according to a certain principle. The unit consists of landslide gene characterization, gene coding and gene feature library.
The gene characterization is characterized by adopting a multi-chromosome method, in a breeding environment, the gene characteristics of topographic features, stratigraphic geology, rock-soil properties, covering layers, weathering characteristics, hydrological characteristics, main physical and mechanical properties of rock-soil, regional faults, weak interlayers, seismic intensity, recent structure motion and the like, and the gene characteristics of meteorological factors such as rainfall, ice and snow, freeze thawing and the like and external environment factors such as blasting vibration, excavation, dynamic/static load and the like can be characterized by a plurality of characteristic parameters, and the state of the gene characteristics depends on the factor as the threshold value of a disaster-causing gene;
the gene coding is characterized in that the characteristic of the environment inoculated inside and outside a landslide is taken as a chromosome, the gene characters are divided into disaster-causing and non-disaster-causing, when the value of the character parameter exceeds a threshold value, the disaster-causing is carried out, otherwise, the disaster-causing is carried out, binary coding is adopted, each gene character is characterized as (0, 1), the combination of genes in different chromosomes forms sequence codes of '0' and '1', the code length is a multiple of 8, and the complement is carried out by 0 when the multiple is insufficient;
the gene feature library is a gene set forming landslide features, each landslide gene feature represents a type of landslide, and the gene features are fingerprints and irises of individual landslides and are basic bases and standards for identifying the landslides.
The landslide identification and prediction unit extracts the genetic characteristics of the side slope through the landslide big data of the region where the side slope is located, and realizes the landslide identification and prediction process through comparing the genetic characteristics of the individual side slope with the genetic sequences of the landslide genetic characteristic library. The system comprises slope big data, gene characteristic extraction, and landslide identification and prediction. The gene sequence is a gene sequence set constructed by encoding landslide characteristic genes according to the gene encoding principle and a certain length (multiple of 8).
The big side slope data is a sky-ground-person data system consisting of weather, topographic geology, regional geological structures, side slope structures and artificial disturbance in the regions where the side slopes are located, comes from real-time monitoring data such as weather satellites, resource environment satellites and seismic table nets and the like, and geological exploration, exploration and human activity data related to the side slopes, and has the characteristics of large quantity, multiple types, isomerism, fast change, uncertainty, nonlinearity, low value density and the like;
the gene characteristic extraction is to divide gene types according to different functions and properties from the congenital conditions of the landslide inoculation environment elements, extract the congenital defect elements, namely defect genes-disaster-causing genes, from the congenital defect elements, and the landslide gene characteristic factors are inoculation environment factors which are obviously expressed in landslide geological disasters;
the landslide identification and prediction is realized by a computer, a printer and an external display unit, wherein the computer analyzes, stores and outputs the big data through a program and identifies and predicts the side slope in real time.
The identification and prediction comprises the processes of slope big data, data preprocessing, feature extraction, feature matching, identification and prediction, and is a process of acquiring big data influencing landslide, preprocessing the data, extracting feature genes from the big data, and comparing the feature genes with genes in a landslide gene feature library so as to identify, evaluate, predict and early warn whether the side slope is landslide or not;
the evaluation is to evaluate the safety and stability of the side slope under the disturbance of the geometrical structure, the topographic geology, the rock-soil characteristics, the geological structure, the weather, the human activity environment and the like of the side slope; the evaluation is to determine the disaster-causing gene and the occurrence condition thereof, and when the state of the characteristic gene is 1 and is matched with the gene sequence of the gene characteristic library, landslide occurs; when the characteristic gene is not matched with the gene sequence of the gene characteristic library, landslide cannot occur; therefore, the influence of the landslide inoculation environment and external factors on the safety and stability of the side slope can be determined, and the direction is provided for the side slope protection.
The prediction and early warning are carried out on the possibility and time of landslide of the side slope, and an alarm is sent out according to the danger degree of the landslide; the possibility and the time of landslide occurrence refer to a landslide event occurring on a side slope and the specific time of occurrence of the landslide event, and the condition of the genetic characteristic corresponding to the landslide characteristic gene sequence and the time of occurrence of the condition are the time of occurrence of the landslide.
The conversion box and the digital strain gauge adopt a CR-655 interface cable to transmit data, the digital strain gauge and the data integration box adopt an RS-232C interface cable to transmit data, and the data integration box and the data analysis unit adopt a CR-553B interface cable to transmit data.
The working voltage of the digital strain gauge is 12V.
The working voltage of the data integration box is 12V.
The invention has the advantages that by adopting the technical scheme, on one hand, weather, earthquake, geology and human activity data of a side slope region are provided by real-time monitoring data of a weather satellite, a resource environment satellite and a seismic platform network and side slope geology and regional geology investigation, so that accurate, quick and efficient identification and prediction of side slope landslide are realized; on the other hand, based on the big data of the side slope, quantitative calculation and analysis are not needed, and the safety and stability evaluation and landslide prediction of the side slope can be realized through real-time data and historical data; through networking with weather, resource environment satellites and the like, after positioning by the position satellites, the landslide geological disaster forecasting can be realized, the operation is simple, the data is massive, and the automation, the intelligent degree and the identification precision are high.
The method organically combines the self condition of the side slope, the slope inoculation environment and external factors, and based on big data and gene identification, the technical bottleneck that the existing landslide prediction is difficult to carry out quantitative and analytic calculation when processing nonlinear, multivariate, heterogeneous, dynamic, random and uncertain data of a complex huge system is overcome, and the efficient fusion and utilization of big data of a space-ground-human large system are realized.
Drawings
FIG. 1 is a structural diagram of the landslide geological disaster gene identification and prediction system of the present invention.
Fig. 2 is a basic feature configuration diagram of landslide macro data in the embodiment of the present invention.
FIG. 3 is a diagram showing the logical relationship between landslide gene identification and prediction in the example of the present invention.
FIG. 4 is a gene coding diagram of meteorological data in an example of the present invention.
FIG. 5 is a gene coding diagram of resource and environment data according to an embodiment of the present invention.
FIG. 6 is a gene coding map of seismic data according to an embodiment of the invention.
FIG. 7 is a gene coding diagram of slope geological data in an embodiment of the invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
As shown in fig. 1, the landslide geological disaster gene identification and prediction system of the invention comprises a slope big data unit, a characteristic data analysis unit and a landslide identification and prediction unit;
the slope big data unit comprises a satellite positioning system, adopts a Beidou satellite system and is used for acquiring position data of a slope, and usually represents the position data by longitude and latitude, coordinates, azimuth angles and direction angles of a slope center or a slope engineering measurement mark point initial position; the meteorological satellite is mainly used for providing real-time meteorological data such as atmospheric temperature, humidity, rainfall intensity, snowfall, snow thickness, freeze-thaw depth and the like in a slope region; the resource environment satellite is mainly used for providing landform and landform, vegetation and related human activities in a side slope region, wherein the landform type, altitude difference, slope height, vegetation type, vegetation coverage, stowage, slope toe excavation and other human engineering activities are included, and side slope parameters such as slope height and slope are also provided by landform data or side slope geological data; the earthquake table net is used for monitoring regional earthquake activity and other vibration caused by non-earthquake, and main data comprises magnitude, acceleration, speed and vibration amplitude; the slope geological data is provided by engineering geological survey and geotechnical engineering survey data, is supplement and deepened of resource environmental data, is used for acquiring data such as a covering layer, a basal layer, geotechnical characteristics, underground water, a fault and the like of a slope area, and mainly comprises a quaternary thickness, a weathered layer thickness, a rock stratum occurrence, lithology, special soil, special rock, underground water burial depth, a fault type and a position relation between the fault type and a slope; the human activities mainly refer to direct or indirect human engineering construction and production activities having an influence on the stability of the side slope, including loads such as stacking, excavation, blasting, vehicle operation and the like, and in the embodiment, the loads are monitored by a resource environment satellite and are corrected by side slope geological survey and manual work.
In this embodiment, the slope big data is uploaded to the characteristic data analysis unit through the cable and the interface.
The characteristic data analysis unit comprises an amplifier and is used for amplifying the signal; the conversion box is used for converting the analog signals in the big data into digital signals; the digital strain gauge is connected with a digital strain gauge through a CR-655 interface cable, and data is input into a data integration box DIB through an RS-232C interface cable; the digital signals in the big data are directly uploaded to the digital integration box DIB by the cable and RS-232C interface.
And the data integration box integrates the data input by the characteristic data analysis unit and inputs the data into the landslide identification and prediction unit through a CR-553B interface cable.
The landslide identification and prediction unit comprises a computer and other terminals and is used for storing, displaying, analyzing, printing and network transmitting data.
As shown in fig. 2, in the embodiment of the present invention, a basic feature configuration diagram of landslide big data is obtained by extracting data from position satellites, meteorological satellites, resource environment satellites, seismic nets and slope geological big data, and integrating, filtering, statistically analyzing and mining the data in a feature data analysis unit to form data types and feature data.
As shown in fig. 3, in the embodiment of the present invention, a landslide gene identification and prediction logical relationship diagram is obtained, a system reads data from a slope big data unit, the data is subjected to preprocessing such as integration, selection by brushing, denoising, and deformation in a feature data analysis unit, landslide gene feature data is extracted according to an encoding principle and compared with a landslide gene feature library, and if a gene feature of a slope is consistent with a gene of the gene feature library and a feature matching relationship is established, it is determined that landslide will occur under the conditions of this embodiment; if the characteristic matching relation does not hold, no landslide occurs; according to the time of the landslide occurrence condition in the embodiment, the specific time of the landslide occurrence of the side slope at the position in the embodiment can be determined, so that the identification and prediction of the side slope landslide are realized.
As shown in fig. 4, in the embodiment of the present invention, the state of the meteorological feature data and the subcode thereof are shown, in this embodiment, the slope is located in the south china, at a certain time in summer, the states of the regional atmospheric temperature, atmospheric humidity, rain intensity, snow fall amount, snow thickness and frozen soil depth are all 0, the rain fall amount reaches the threshold, the state is 1, and the subcode is: 0010000.
as shown in fig. 5, in the embodiment of the present invention, the genetic code map of the resource and environment data has a relatively flat topography, a height difference and a slope state of 0, a vegetation type and coverage state of 0, and during human activities, the state of stacking and vibration is 0, but the excavation of the slope toe is not protected in time, the state is 1, the slope is a high and steep slope, the slope height exceeds 100 meters, the slope reaches 47 °, the slope height and slope state is 1, and the subcode is: 000001011.
as shown in fig. 6, in the seismic data gene encoding diagram in the embodiment of the present invention, the region where the side slope is located is a low seismic region, the seismic signal monitored by the seismic table network is weak, the seismic magnitude state is 0, and the sub-code is: 0.
as shown in fig. 7, in the embodiment of the present invention, the slope geological data gene coding map is a quaternary blanket, the dip of the basement rock stratum is consistent with the slope dip, the slope is collapsible soil, the groundwater burial depth is shallow, the slope region contains a forward fault fracture zone, and the fault is relatively developed; in this embodiment, the state of the elements of the quaternary cover layer, the dip angle, the special soil, the ground water level and the distance 5 from the fault to the side slope is 1, the other states are 0, and the subcode is: 1001010101.
in this example, the landslide gene code sequence is: since the sub-code 1+ sub-code 2+ sub-code 3+ sub-code 4 is 001000000000101101001010101X, 27 bits in total, and a binary computer code is used, and the 28 th bit X is a missing bit and is replaced by 0, the gene code of the landslide in this embodiment is: 0010000000001011010010101010.
Claims (2)
1. a landslide geological disaster gene identification and prediction system is characterized by comprising a side slope big data unit, a characteristic data analysis unit and a landslide identification and prediction unit;
the slope big data unit is a system for extracting, preprocessing and converting sky-ground-person data influencing landslide, and is used for acquiring, extracting, inputting and preprocessing slope position, meteorological data, topographic geology, regional geological structure, slope structure and artificial disturbance data and providing original data for genetic identification and prediction of landslide; wherein:
the slope position mainly comes from a satellite positioning system, and the main parameters comprise longitude and latitude, three-dimensional coordinates, an azimuth angle and a direction angle of the geographic position of the slope;
the weather data mainly come from a weather satellite and are dynamic real-time data, the weather data comprise changes of wind, frost, rain and snow, and the parameters mainly comprise atmospheric temperature, atmospheric humidity, rainfall intensity, snow thickness and frozen soil depth;
the topographic and geological data mainly come from resource environment satellites and are corrected by topographic and geological survey data and geotechnical engineering survey data of the slope field, and the parameters mainly comprise the type and the gradient of the slope, the lithology of the slope stratum, the type and the thickness of a rock body, underground water and the inclination angle of the basement stratum;
the regional geological structure data mainly come from a seismic table network and regional geological survey, and the parameters mainly comprise faults, fault distribution, fault grades and the relation between the fault grades and slopes, seismic magnitude and seismic intensity;
the side slope structure data mainly come from resource environment satellites and side slope design or side slope geological survey data, and the parameters mainly include the height, the gradient and the inclination of a side slope and steps;
the human activity data mainly come from a resource environment satellite, and the parameters mainly comprise the load size and strength, slope toe excavation, blasting, vehicle running and the like;
the characteristic data analysis unit consists of a conversion box, an amplifier, a digital strain gauge and a data integration box; the analog signals are converted into digital signals through a conversion box and then enter a Data Integration Box (DIB) through the digital strain gauge, the vibration signals and the audio signals are amplified through the amplifier and then enter the data integration box through the digital strain gauge, and the temperature, humidity, terrain and slope information directly enter the data integration box to be subjected to data analysis and integration and then uploaded to the landslide identification and prediction unit;
the characteristic data analysis is to characterize the landslide gene attribute by the landslide gene characteristic extracted from big data influencing landslide through the existing research results and literature analysis, and establish a gene characteristic library after gene coding is carried out according to the scientific principle; the unit consists of landslide gene characterization, gene coding and a gene feature library;
the gene characterization is characterized by adopting a multi-chromosome method, in a breeding environment, the gene characteristics of topographic features, stratigraphic geology, rock-soil properties, covering layers, weathering characteristics, hydrological characteristics, main physical and mechanical properties of rock-soil, regional faults, weak interlayers, seismic intensity, recent structure motion and the like, and the gene characteristics of meteorological factors such as rainfall, ice and snow, freeze thawing and the like and external environment factors such as blasting vibration, excavation, dynamic/static load and the like can be characterized by a plurality of characteristic parameters, and the state of the gene characteristics depends on the factor as the threshold value of a disaster-causing gene;
the gene coding is characterized in that the characteristic of the environment inoculated inside and outside a landslide is taken as a chromosome, the gene characters are divided into disaster-causing and non-disaster-causing, when the value of the character parameter exceeds a threshold value, the disaster-causing is carried out, otherwise, the disaster-causing is carried out, binary coding is adopted, each gene character is characterized as (0, 1), the combination of genes in different chromosomes forms sequence codes of '0' and '1', the code length is a multiple of 8, and the complement is carried out by 0 when the multiple is insufficient;
the gene characteristic library is a gene set forming landslide characteristics, each landslide gene characteristic represents a type of landslide, and the gene characteristics are fingerprints and irises of individual landslides and are basic basis and standard for identifying the landslides;
the landslide identification and prediction unit extracts the genetic characteristics of the side slope through landslide big data of the area where the side slope is located, and realizes the landslide identification and prediction process through comparison of the genetic characteristics of the individual side slope and a landslide genetic characteristic library; the system comprises slope big data, gene characteristic extraction, landslide identification and prediction;
the big side slope data is a sky-ground-person data system consisting of weather, topographic geology, regional geological structures, side slope structures and artificial disturbance in the regions where the side slopes are located, real-time monitoring data from weather satellites, resource environment satellites, seismic networks and the like, and geological exploration, exploration and human activity data related to the side slopes, and has the characteristics of large quantity, multiple types, isomerism, fast change, uncertainty, nonlinearity and the like;
the gene characteristic extraction is to divide gene types according to different functions and properties from the congenital conditions of the landslide inoculation environment elements, extract the congenital defect elements, namely defect genes-disaster-causing genes, from the congenital defect elements, and the landslide gene characteristic factors are inoculation environment factors which are obviously expressed in landslide geological disasters;
the landslide identification and prediction is carried out by a computer, a printer and an external display unit, wherein the computer analyzes, stores and outputs the big data through a program and carries out identification and prediction on a side slope in real time;
the identification and prediction comprises the processes of slope big data, data preprocessing, feature extraction, feature matching, identification and prediction, and is a process of acquiring big data influencing landslide, preprocessing the data, extracting feature genes from the big data, and comparing the feature genes with genes in a landslide gene feature library so as to identify, evaluate, predict and early warn whether the side slope is landslide or not;
the evaluation is to evaluate the safety and stability of the side slope under the disturbance of the geometrical structure, the topographic geology, the rock-soil characteristics, the geological structure, the weather, the human activity environment and the like of the side slope;
the prediction and early warning are used for estimating the possibility and time of landslide of the side slope and giving an alarm according to the danger degree of the landslide.
2. The landslide geological disaster gene identification and prediction system of claim 1 wherein the slope big data unit further comprises slope real-time monitoring data obtained using various sensors and other remote sensing technologies.
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