CN117893382A - Geological disaster risk evaluation method and system based on multi-element feature fusion - Google Patents
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Abstract
The invention discloses a geological disaster risk evaluation method and a geological disaster risk evaluation system based on multi-element feature fusion, and relates to the field of geological disaster risk evaluation. According to the invention, the multisource data is effectively combined, so that the precise risk assessment of the geological disasters is realized, the accuracy and the reliability of the risk assessment of the geological disasters are improved, and scientific basis is provided for the management, monitoring, forecasting and formulation of disaster relief emergency measures and environmental protection of the geological disasters.
Description
Technical Field
The invention relates to the technical field of geological disaster risk evaluation, in particular to a geological disaster risk evaluation method and system based on multi-element feature fusion.
Background
The geological disaster risk evaluation is a multi-dimensional process, and involves comprehensive analysis and evaluation of potential hazards, human exposure and vulnerability of geological disasters. The method for evaluating the risk of the geological disaster is mainly divided into qualitative evaluation and quantitative evaluation. Qualitative evaluation is based on expert experience and judgment, while quantitative evaluation is based on mathematical models and statistical analysis. In addition, there are other methods and tools that can be used for geologic hazard risk assessment. The geological disaster risk evaluation plays an important role in national economy and development, and provides scientific basis for national resource planning, major engineering site selection and geological disaster management.
The risk evaluation of the geological disaster comprises the following steps of 1. The potential evaluation of the geological disaster is carried out by evaluating the occurrence possibility of the potential geological disaster, and factors such as geological structure, geological material, topography and the like are generally considered. 2. Exposure assessment, which is to evaluate the exposure of humans and property at the time of geological disasters, including the distribution of human activities and population density, and the distribution and value of property. 3. Vulnerability assessment, which is to consider the resistance and adaptability of humans and property to geological disasters and the recovery and reconstruction ability after disaster. And combining the results of the three evaluation aspects to obtain the risk level and the risk distribution map of the geological disaster. And corresponding countermeasures and early warning systems are formulated according to the evaluation results so as to reduce the loss of the geological disasters to human lives and properties.
Disclosure of Invention
In order to solve the technical problems, the invention provides a geological disaster risk evaluation method and system based on multi-element feature fusion.
The first aspect of the invention provides a geological disaster risk evaluation method based on multi-element feature fusion, which comprises the following steps:
the method comprises the steps of obtaining multi-source monitoring data of a target area, preprocessing the multi-source monitoring data to obtain occurrence probability of potential geological disasters, generating occurrence probability of the geological disasters, and screening hidden danger points according to the occurrence probability of the geological disasters;
analyzing the movement path and the sliding distance of the geological disaster according to the geographical condition of the position of the hidden danger point, and judging the probability of reaching the disaster-bearing body corresponding to the geological disaster based on the sliding distance and the geographical information of the dangerous object;
acquiring a disaster causing range of a geological disaster, acquiring space-time probability of a disaster-bearing body according to time of the disaster-bearing body in the disaster causing range, and evaluating vulnerability and value of the disaster-bearing body according to basic information and structural characteristics of the disaster-bearing body;
and constructing a risk factor set according to the occurrence probability of the geological disasters, the arrival disaster-bearing body probability, the disaster-bearing body space-time probability, the vulnerability of the disaster-bearing body and the disaster-bearing body value, and evaluating the geological disaster risk of the target area based on the risk factor set by using an annual risk mode.
In this scheme, carry out the preliminary treatment with multisource monitoring data and acquire the probability of occurrence of potential geological disasters, generate geological disasters probability of occurrence, specifically do:
acquiring multisource monitoring data of a target area, performing data cleaning on the retrieved multisource monitoring data, and screening precipitation data and rock-soil structural characteristics from the preprocessed multisource monitoring data;
accessing a related geological disaster database, establishing a retrieval tag according to GIS data corresponding to a target area, acquiring geological disaster examples in the target area, and reading the daily maximum precipitation under different preset reproduction periods by adopting a generalized extremum distribution function based on precipitation data;
extracting precipitation critical values corresponding to different geological disaster stable states and unstable states in the geological disaster example, acquiring an accumulated probability distribution function according to the precipitation critical values, and predicting the occurrence probability of the daily maximum precipitation reproduction period by using the accumulated probability distribution function of the precipitation critical values as the rainfall induction probability;
clustering according to the rock-soil structural features of different geological disaster examples, obtaining geological disaster example clusters corresponding to the different rock-soil structural features, judging example data amounts of the different clusters, and eliminating the geological disaster example clusters which do not meet the preset data amount standard;
performing disaster space-time evolution analysis in the screened geological disaster instance clusters, extracting dominant factor sets of different geological disaster instance clusters, and performing stability factor selection in the dominant factor sets by using a Fisher algorithm;
judging whether pearson correlation coefficients of different dominant factors and stability meet preset requirements or not by taking the maximum geological disaster recognition accuracy as a target, and acquiring dominant factors meeting the preset requirements as factors influencing the stability of the rock-soil body;
and obtaining geological disaster instability probabilities of different rock-soil structure areas in the target area by utilizing a Monte Carlo method through factors influencing the stability of the rock-soil body, and obtaining the occurrence probability of the geological disaster by combining the geological disaster instability probabilities with the rainfall induction probability.
In the scheme, the probability of arriving at the disaster-bearing body corresponding to the geological disaster is judged, and the probability is specifically:
acquiring a rock-soil structure characteristic of a position where a hidden trouble point is located, carrying out initial hidden trouble assessment based on the rock-soil structure characteristic, determining a neighborhood region range corresponding to the hidden trouble point, and carrying out gridding treatment on the neighborhood region range;
extracting geographic conditions of hidden danger points through geological survey data of a target region, performing similarity calculation in a neighborhood grid according to the geographic conditions, and selecting the neighborhood grid meeting a preset similarity standard for marking to serve as a related hidden danger point;
acquiring hidden danger points and the regional range of the grid where the hidden danger points are located and the rock-soil structural characteristics of the regional range as hidden danger characteristics, and screening similar geological disaster examples meeting corresponding standards from geological disaster examples by utilizing the hidden danger characteristics;
and carrying out integration analysis according to the similar geological disaster examples to obtain a motion path and a sliding distance of the landslide of the hidden danger point, determining the position information of the disaster-affected object in the target area, and obtaining the probability of the geological disaster reaching the disaster-bearing body according to the structural characteristics of the side slope of the hidden danger point by adopting a sliding distance method.
In the scheme, a disaster causing range of a geological disaster is obtained, and the space-time probability of the disaster bearing body is obtained according to the time of the disaster bearing body in the disaster causing range, specifically:
determining a disaster causing range of the geological disaster according to the movement path and the sliding distance of the geological disaster at the hidden danger point, and dividing a disaster bearing body in the target area into a fixed disaster bearing body and a movable disaster bearing body according to whether the disaster bearing body can move or not;
acquiring a threat range of a geological disaster at a hidden trouble point by utilizing the disaster causing range, judging whether the fixed disaster bearing body is in the threat range, and acquiring space-time probability of the fixed disaster bearing body according to a judging result;
and acquiring natural properties of landslide bodies corresponding to the geological disasters at hidden danger points, early warning signals, evacuation systems of target areas and the movement capacities of disaster-bearing bodies and personnel as adjustment parameters, and calculating the space-time probability of the movable disaster-bearing bodies according to the time of the movable disaster-bearing bodies in the disaster-causing range.
In the scheme, the vulnerability and the value of the disaster-bearing body are evaluated according to the basic information and the structural characteristics of the disaster-bearing body, and the method specifically comprises the following steps:
acquiring vulnerability evaluation indexes according to self-inherent properties of disaster-bearing bodies to construct a vulnerability evaluation model, wherein the vulnerability evaluation model is expressed as，/>Is vulnerability of disaster-bearing bodies; />Is an index of vulnerability;
taking the structure type, deformation condition and service life as building vulnerability evaluation indexes, and taking the personnel physical condition, personnel age structure and geological disaster cognition level as personnel vulnerability evaluation indexes;
information expansion is carried out in a preset search space through data search by utilizing vulnerability evaluation indexes of buildings and personnel, descriptive texts of the evaluation indexes are obtained, word segmentation is carried out on the descriptive texts to obtain word vectors, and descriptive keywords are obtained according to occurrence frequency of the word vectors;
generating text vectors of descriptive keywords based on context through Bert training, simplifying descriptive texts according to the text vectors, and obtaining descriptions and index values of vulnerability evaluation indexes;
and estimating the value of the disaster-bearing body of the building and the traffic route according to the structure, the area and the length, and acquiring the value loss of the disaster-bearing body by utilizing the ratio of the value of the disaster-bearing body suffering from disaster damage loss to the value of the disaster-bearing body before disaster.
In the scheme, the annual risk mode is utilized to evaluate the geological disaster risk of the target area based on the risk factor set, and the method specifically comprises the following steps:
calculating property annual loss and personnel annual death probability according to the occurrence probability of geological disasters, the probability of reaching a disaster-bearing body, the space-time probability of the disaster-bearing body, the vulnerability of the disaster-bearing body and the value of the disaster-bearing body as risk factors;
the loss of property for years is expressed as；
The probability of death of a person over the years is expressed as；
Wherein,for property annual loss, < >>For the single annual death probability->For the probability of occurrence of geological disasters +.>For the probability of geological disasters reaching disaster-bearing bodies, < +.>Space-time probability for a fixed or mobile disaster-bearing body, < + >>Is easy to wear and tear on property>For disaster tolerance, the person is treated with->Is vulnerable to personnel.
The second aspect of the invention also provides a geological disaster risk evaluation system based on multi-element feature fusion, which comprises: the device comprises a memory and a processor, wherein the memory comprises a geological disaster risk evaluation method program based on multi-element feature fusion, and the geological disaster risk evaluation method program based on multi-element feature fusion realizes the following steps when being executed by the processor:
the method comprises the steps of obtaining multi-source monitoring data of a target area, preprocessing the multi-source monitoring data to obtain occurrence probability of potential geological disasters, generating occurrence probability of the geological disasters, and screening hidden danger points according to the occurrence probability of the geological disasters;
analyzing the movement path and the sliding distance of the geological disaster according to the geographical condition of the position of the hidden danger point, and judging the probability of reaching the disaster-bearing body corresponding to the geological disaster based on the sliding distance and the geographical information of the dangerous object;
acquiring a disaster causing range of a geological disaster, acquiring space-time probability of a disaster-bearing body according to time of the disaster-bearing body in the disaster causing range, and evaluating vulnerability and value of the disaster-bearing body according to basic information and structural characteristics of the disaster-bearing body;
and constructing a risk factor set according to the occurrence probability of the geological disasters, the arrival disaster-bearing body probability, the disaster-bearing body space-time probability, the vulnerability of the disaster-bearing body and the disaster-bearing body value, and evaluating the geological disaster risk of the target area based on the risk factor set by using an annual risk mode.
The invention discloses a geological disaster risk evaluation method and a geological disaster risk evaluation system based on multi-element feature fusion, and relates to the field of geological disaster risk evaluation. According to the invention, the multisource data is effectively combined, so that the precise risk assessment of the geological disasters is realized, the accuracy and the reliability of the risk assessment of the geological disasters are improved, and scientific basis is provided for the management, monitoring, forecasting and formulation of disaster relief emergency measures and environmental protection of the geological disasters.
Drawings
FIG. 1 shows a flow chart of a geological disaster risk assessment method based on multi-feature fusion of the invention;
FIG. 2 shows a flow chart for judging the probability of reaching a disaster-bearing body corresponding to a geological disaster;
FIG. 3 is a flow chart illustrating the assessment of vulnerability and value of disaster-tolerant bodies according to the present invention;
fig. 4 shows a block diagram of a geological disaster risk assessment system based on multi-feature fusion according to the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
Fig. 1 shows a flowchart of a geological disaster risk evaluation method based on multi-feature fusion.
As shown in fig. 1, the first aspect of the present invention provides a geological disaster risk evaluation method based on multi-feature fusion, which includes:
s102, acquiring multi-source monitoring data of a target area, preprocessing the multi-source monitoring data to acquire occurrence probability of potential geological disasters, generating occurrence probability of the geological disasters, and screening hidden danger points according to the occurrence probability of the geological disasters;
s104, analyzing a movement path and a sliding distance of the geological disaster according to the geographical condition of the position of the hidden danger point, and judging the probability of reaching a disaster-bearing body corresponding to the geological disaster based on the sliding distance and the geographical information of the dangerous object;
s106, acquiring a disaster causing range of the geological disaster, acquiring space-time probability of the disaster-bearing body according to the time of the disaster-bearing body in the disaster causing range, and evaluating vulnerability and value of the disaster-bearing body according to basic information and structural characteristics of the disaster-bearing body;
s108, constructing a risk factor set according to the occurrence probability of the geological disaster, the probability of reaching the disaster-bearing body, the space-time probability of the disaster-bearing body, the vulnerability of the disaster-bearing body and the value of the disaster-bearing body, and evaluating the geological disaster risk of the target area based on the risk factor set by using an annual risk mode.
The method comprises the steps of acquiring multi-source monitoring data of a target area by utilizing monitoring points such as rainfall, groundwater, geographic structure and deformation, cleaning the retrieved multi-source monitoring data, and screening precipitation data and rock-soil structural characteristics from the preprocessed multi-source monitoring data; accessing a related geological disaster database, establishing a retrieval tag according to GIS data corresponding to a target area, acquiring geological disaster examples in the target area, and reading the daily maximum precipitation under different preset reproduction periods by adopting a generalized extremum distribution function based on precipitation data; extracting precipitation critical values corresponding to different geological disaster stable states and unstable states in the geological disaster instance, when the geological disaster situation occurs, the daily maximum precipitation of the day is necessarily larger than the precipitation critical value, the precipitation critical value is regarded as the same as the daily maximum precipitation, the daily maximum precipitation is assumed to obey the extreme value I-type distribution, and the precipitation critical value is calculated according to the precipitation critical valueAcquiring cumulative probability distribution function->Predicting the occurrence probability of the daily maximum precipitation reproduction period by using the cumulative probability distribution function of the precipitation threshold value +.>As rainfall-inducing probability->；
Clustering according to the rock-soil structural features of different geological disaster examples, obtaining geological disaster example clusters corresponding to the different rock-soil structural features, judging example data amounts of the different clusters, and eliminating the geological disaster example clusters which do not meet the preset data amount standard; in the screened geological disaster instance clusterAnalyzing the space-time evolution of disasters, judging the development condition of landslide cracks, extracting dominant factor sets of different geological disaster instance clusters by combining natural factors and slope structural features, and selecting stability factors from the dominant factor sets by using a Fisher algorithm; judging whether pearson correlation coefficients of different dominant factors and stability meet preset requirements or not by taking the maximum geological disaster recognition accuracy as a target, and acquiring dominant factors meeting the preset requirements as factors influencing the stability of the rock-soil body; the geological disaster instability probability of different rock-soil structure areas in a target area is obtained by utilizing a Monte Carlo method through factors influencing the stability of a rock-soil body, and the geological disaster instability probability and the rainfall induction probability are combined to obtain the occurrence probability of the geological disaster。
Assume thatIs a state function of slope->As a function of the random variable, e.g. the volume weight of the soil mass +.>Cohesive force->Angle of internal friction->And the like, which influence the stability of the rock-soil body. Randomly extracting to form a group of samples, substitutingObtain->A set of samples is randomly extracted again and is taken +.>Obtain->Thus get +.>Are relatively independentValues, forming a new set of sample values +.>，/>,...,/>. Based on this, assume +.>Is the safety critical value, in->There is +.>The number of (2) is->The probability of instability of the ramp is +.>。
Fig. 2 shows a flow chart for judging the probability of reaching a disaster-bearing body corresponding to a geological disaster according to the invention.
According to the embodiment of the invention, the probability of reaching the disaster-bearing body corresponding to the geological disaster is judged, specifically:
s202, acquiring rock-soil structural features of positions of hidden danger points, carrying out initial hidden danger assessment based on the rock-soil structural features, determining a neighborhood region range corresponding to the hidden danger points, and carrying out gridding treatment on the neighborhood region range;
s204, extracting geographic conditions of hidden danger points through geological survey data of a target region, performing similarity calculation in a neighborhood grid according to the geographic conditions, and selecting the neighborhood grid meeting a preset similarity standard for marking to serve as a related hidden danger point;
s206, obtaining hidden danger points and the regional range of the grid where the hidden danger points are located and the rock-soil structural characteristics of the regional range as hidden danger characteristics, and screening similar geological disaster examples meeting corresponding standards from geological disaster examples by utilizing the hidden danger characteristics;
s208, carrying out integration analysis according to the similar geological disaster examples to obtain a motion path and a sliding distance of the landslide of the hidden danger point, determining the position information of the disaster-stricken object in the target area, and obtaining the probability of the geological disaster reaching the disaster-bearing body according to the structural characteristics of the side slope of the hidden danger point by adopting a sliding distance method.
The probability of the geological disaster reaching the disaster-bearing body is the probability of the landslide reaching a certain disaster-bearing body in the influence area, and the arrival probability of the landslide depends on the respective positions of the landslide body and the dangerous object, the possible motion path and the possible sliding distance of the landslide. The current commonly used arrival probability estimation methods are an empirical method, a statistical method and a sliding distance method. And (3) carrying out initial hidden danger assessment by using the methods such as expert experience assessment and the like and using the rock-soil structural characteristics, wherein the larger the initial hidden danger is, the larger the disaster sweep range is, and the larger the corresponding neighborhood region range is. When hidden danger features of hidden danger points are similar to the features of the neighborhood regions, the large probability of the neighborhood regions is related when the hidden danger points are subjected to geological disasters, integration analysis is carried out by utilizing historical geological disaster examples, space-time development rules of the historical geological disasters are obtained through methods such as machine learning, and determination of movement paths and sliding distances of landslide of the hidden danger points is carried out. The estimation formula of the hidden danger point slope collapse area range is as follows:，/>for the horizontal projection distance from the outer edge of the slope top collapse zone to the slope bottom edge of the slope, the +.>Is a slope with a high slope>Is the broken angle of the slope. For the inclined soil slope, take +.>Taking +.>，/>Is a slope angle of a side slope->The probability that a geological disaster reaches a disaster-bearing body is the internal friction angle of the soil body，/>Is the distance between the toe and the disaster-bearing body.
The disaster-causing range of the geological disaster is determined according to the movement path and the sliding distance of the geological disaster at the hidden danger point, and the disaster-bearing body in the target area is divided into a fixed disaster-bearing body and a movable disaster-bearing body according to whether the disaster-bearing body can move or not; the fixed disaster-bearing body comprises house, highway, railway and other immovable public and civil facilities, and the movable disaster-bearing body comprises personnel, vehicles, livestock and the like. Acquiring a threat range of a geological disaster at a hidden danger point by utilizing the disaster causing range, wherein the threat range is slightly larger than the disaster causing range, judging whether the fixed disaster bearing body is in the threat range, acquiring the space-time probability of the fixed disaster bearing body according to a judging result, wherein the space-time probability of the disaster bearing body is 1 in the threat range, and otherwise, the space-time probability is 0;
the mobile disaster-bearing body has the characteristic of mobile, and the time-space probability of the mobile disaster-bearing body mainly depends on the time in the disaster-causing range of the disaster. The spatiotemporal probability can be calculated typically with 1 person averaging the time in the disaster causing range. For example, 1 person is at home for 300 days each year and 12 hours each day at home, the spatiotemporal probability is (300/365) × (12/24) =0.41. For a single vehicle running under a landslide, its time-space probability is the proportion of time it takes to travel on the road under the landslide in a year. For all vehicles passing under a single landslide, the time-space probability is the sum of the times in a year that a single vehicle passes the path under the landslide. For personnel within the vehicle, the spatiotemporal probability is the same as the vehicle probability. However, the space-time probabilities are different for one person and four persons in a car. In some cases, consideration should be given to estimating the spatio-temporal probability if the affected person is sufficiently alert and can withdraw from the affected area in time. People above the landslide body can observe the sliding of the landslide body more easily than people below the landslide body and on the landslide body, and the people need to withdraw in time.
And acquiring natural properties (volume and speed) of the landslide body corresponding to the geological disaster at the hidden danger point, early warning signals, an evacuation system of a target area and the movement capability of the disaster-bearing body and personnel as adjustment parameters, and calculating the space-time probability of the disaster-bearing body according to the time of the disaster-bearing body in the disaster-causing range. According to the disaster-bearing body and the conditions, the probability of people at home in daytime is smaller than that of people at night; the probability of being at work or school during the day is greater than the probability of being at night. For young and strong years, the probability of being at home for 1 month, 2 months and 12 months each year is high, and the probability of being at home for 3 to 11 months is low; for elderly children, the probability at home is relatively average. Under rainfall conditions, the probability of personnel in the house is higher than usual under the condition of no early warning evacuation notification.
FIG. 3 shows a flow chart of the present invention for evaluating vulnerability and value of disaster-tolerant bodies.
According to the embodiment of the invention, the vulnerability and the value of the disaster-bearing body are evaluated according to the basic information and the structural characteristics of the disaster-bearing body, and the method specifically comprises the following steps:
s302, acquiring vulnerability evaluation indexes according to the inherent attribute of the disaster-bearing body to construct a vulnerability evaluation model; the vulnerability assessment model is expressed as，/>Is vulnerability of disaster-bearing bodies; />Is an index of vulnerability;
s304, taking the structure type, deformation condition and service life as building vulnerability evaluation indexes, and taking the personnel physical condition, personnel age structure and geological disaster cognition level as personnel vulnerability evaluation indexes;
s306, carrying out information expansion by utilizing vulnerability evaluation indexes of buildings and personnel in a preset retrieval space through data retrieval to obtain descriptive texts of the evaluation indexes, carrying out word segmentation on the descriptive texts to obtain word vectors, and obtaining descriptive keywords according to occurrence frequency of the word vectors;
s308, generating text vectors of descriptive keywords based on context through Bert training, and simplifying descriptive texts according to the text vectors to obtain descriptions and index values of vulnerability evaluation indexes;
s310, estimating the value of the disaster-bearing body of the building and the traffic route according to the structure, the area and the length, and acquiring the value loss of the disaster-bearing body by utilizing the ratio of the value of the disaster-bearing body suffering from disaster damage loss to the value of the disaster-bearing body before disaster.
In the case of quantitatively calculating the risk, the vulnerability is based on probability, and thus a range of 0 to 1 is used. Building vulnerability can be considered in terms of structure type, deformation, and age 3. The structure types can indirectly reflect the disaster resistance of the building, and the building structure types are classified by the material classification of the bearing members of the building and the common bearing member types in the actual building. Deformation is an assessment of the current condition of a building, and the vulnerability exhibited by different deformation conditions is different. The service life and design life of the building are important reflection indexes for the self-resistance to geological disaster level, and are important indexes for vulnerability.
People are distinguished from buildings in that people have consciousness and mobility, and improving risk consciousness is a primary condition that people successfully avoid geological disasters, and the earlier the people perceive the geological disasters, the greater the probability of avoiding the disasters. Meanwhile, the disaster early warning system can assist in increasing geological disaster dangerous consciousness of people. After having the geological disaster risk awareness, whether the geological disaster can be successfully avoided depends on the action energy of the person and the action capacity of the person. Healthy people have stronger walking ability than people with diseases, and young and old people have stronger walking ability than old people and children. The stronger the mobility, the higher the probability of successfully evading the disaster. Therefore, the vulnerability of the personnel considers 3 indexes of personnel physical condition, personnel age structure and geological disaster cognitive level. The physical condition of the personnel is mainly considered whether the mobility of the personnel is influenced, so that the physical condition of the personnel is divided into 3 types of healthy, sub-healthy and unhealthy, and the age is a main factor for limiting the mobility of the personnel. Generally, the old and children have poor mobility, the young and the young have strong mobility, and the casualties rate of the population of 20-40 years is the lowest when suffering from geological disasters. Therefore, the age structure of the personnel is divided into 3 sections of 20 to 40 years old, 10 to 19 years old or 40 to 60 years old, 0 to 10 years old or >60 years old; the geological disaster cognitive level comprises 2 aspects of identification of early symptoms of the geological disaster and later avoidance route selection. Firstly, people need to know the geological disasters correctly, and the phenomenon before the occurrence of the geological disasters is known; and secondly, selecting a reasonable escape route according to the knowledge of geological disasters. Comprehensively considering the 2 points to divide the cognitive level index of the personnel geological disasters into: (1) The cognition level is high, namely the geological disasters are accurately recognized, precursor information of the disasters can be judged, and escape routes when the disasters occur are known; (2) The cognition level is moderate, namely the cognition level on the geological disasters is general, and the precursor information and the planned escape route of some simple geological disasters are known through the training of later professionals; (3) The cognitive level is low, namely, the cognitive level is not known to geological disasters, and precursor information and a planned escape route of the occurrence of the geological disasters are not known.
Word segmentation is carried out on descriptive texts to obtain word vectors, data statistics is carried out on the word vectors, texts with the highest occurrence frequency are selected, text vectors with description keywords based on contexts are generated through Bert training for texts with the same occurrence frequency, semantic features of the text vectors are utilized to simplify the description texts, for example, steel structures are low in vulnerability, and bearing components are made of steel materials; the serious deformation corresponds to the inclination of the wall body or the occurrence of a crack, and the ground of the house bulges.
The value of the threatening building and the disaster-bearing body of the traffic route are estimated, the building value is estimated according to the area, and the structure mainly can be divided into a steel structure, a brick-concrete structure, a brick-wood structure and a adobe structure. The traffic route value is estimated according to the length, and can be mainly divided into expressways, national roads, provincial roads, general roads, municipal roads and others (villages) and the disaster-bearing body value is estimated according to the corresponding unit price. The loss of the disaster-bearing body value is generated by the damage of disaster-bearing body components and performance (functions), the loss rate of the disaster-bearing body value refers to the ratio of the disaster-bearing body value suffered by disaster damage loss to the disaster-bearing body value before disaster, the loss rate of the disaster-bearing body value is important data for accounting expected loss, and the value loss rate formula is that,For property annual loss, < >>Is a disaster-bearing body value.
The method is characterized in that property annual loss and personnel annual death probability are calculated according to geological disaster occurrence probability, disaster-bearing body arrival probability, disaster-bearing body space-time probability, disaster-bearing body vulnerability and disaster-bearing body value as risk factors;
the loss of property for years is expressed as；
The probability of death of a person over the years is expressed as；
Wherein,is propertyAnnual loss (I)>For the single annual death probability->For the probability of occurrence of geological disasters +.>For the probability of geological disasters reaching disaster-bearing bodies, < +.>Space-time probability for a fixed or mobile disaster-bearing body, < + >>Is easy to wear and tear on property>For disaster tolerance, the person is treated with->Is vulnerable to personnel.
The risk level of setting the annual death probability and the annual loss of property is defined as shown in the following tables 1 and 2:
；
wherein L is low in risk, M is high in risk, H is high in risk, and VH is very high in risk. The risk level is defined by adopting the principle of "about high and about low", and the risk level is defined according to the above tables 1 and 2, and the final risk evaluation level is the one with the higher level.
The method comprises the steps of obtaining secondary disasters in different types of geological disaster examples, constructing a secondary disaster event chain according to the geological disasters and the space-time development characteristics of the secondary disasters, constructing a geological disaster related database, and storing the secondary disaster event chain into the database; when the geological disaster risk of the hidden danger point is larger than a preset risk threshold, determining a secondary disaster monitoring range according to the disaster causing range; acquiring regional characteristics according to vulnerability indexes of disaster-bearing bodies in a secondary disaster monitoring range and regional environment monitoring characteristics, searching in a related database through the regional characteristics, and extracting secondary disaster categories with similarity meeting preset standards for marking; the method comprises the steps of obtaining an environment monitoring sequence in a secondary disaster monitoring range, positioning in event chains marked with secondary disaster categories according to the environment monitoring sequence, removing event chains without positioning points, obtaining time sequence characteristics of the environment monitoring sequence, predicting environment monitoring data after preset time by using the time sequence characteristics, calculating similarity according to the predicted environment monitoring data at next event node of a certain point in the reserved event chains, selecting the event chain with highest similarity, obtaining the corresponding secondary disaster category, and obtaining a disaster optimal plan according to the secondary disaster category. Accuracy of emergency decision and efficiency of emergency command of the geological disaster corresponding to the secondary disaster are improved, and secondary loss caused by the secondary disaster is reduced.
Fig. 4 shows a block diagram of a geological disaster risk assessment system based on multi-feature fusion according to the present invention.
The second aspect of the present invention also provides a geological disaster risk evaluation system 4 based on multi-element feature fusion, which comprises: the memory 41 and the processor 42, wherein the memory comprises a geological disaster risk evaluation method program based on multi-element feature fusion, and the geological disaster risk evaluation method program based on multi-element feature fusion realizes the following steps when being executed by the processor:
the method comprises the steps of obtaining multi-source monitoring data of a target area, preprocessing the multi-source monitoring data to obtain occurrence probability of potential geological disasters, generating occurrence probability of the geological disasters, and screening hidden danger points according to the occurrence probability of the geological disasters;
analyzing the movement path and the sliding distance of the geological disaster according to the geographical condition of the position of the hidden danger point, and judging the probability of reaching the disaster-bearing body corresponding to the geological disaster based on the sliding distance and the geographical information of the dangerous object;
acquiring a disaster causing range of a geological disaster, acquiring space-time probability of a disaster-bearing body according to time of the disaster-bearing body in the disaster causing range, and evaluating vulnerability and value of the disaster-bearing body according to basic information and structural characteristics of the disaster-bearing body;
and constructing a risk factor set according to the occurrence probability of the geological disasters, the arrival disaster-bearing body probability, the disaster-bearing body space-time probability, the vulnerability of the disaster-bearing body and the disaster-bearing body value, and evaluating the geological disaster risk of the target area based on the risk factor set by using an annual risk mode.
The third aspect of the present invention also provides a computer readable storage medium, wherein the computer readable storage medium includes a geological disaster risk evaluation method program based on multi-element feature fusion, and when the geological disaster risk evaluation method program based on multi-element feature fusion is executed by a processor, the steps of the geological disaster risk evaluation method based on multi-element feature fusion are implemented.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A geological disaster risk evaluation method based on multi-element feature fusion is characterized by comprising the following steps:
the method comprises the steps of obtaining multi-source monitoring data of a target area, preprocessing the multi-source monitoring data to obtain occurrence probability of potential geological disasters, generating occurrence probability of the geological disasters, and screening hidden danger points according to the occurrence probability of the geological disasters;
analyzing the movement path and the sliding distance of the geological disaster according to the geographical condition of the position of the hidden danger point, and judging the probability of reaching the disaster-bearing body corresponding to the geological disaster based on the sliding distance and the geographical information of the dangerous object;
acquiring a disaster causing range of a geological disaster, acquiring space-time probability of a disaster-bearing body according to time of the disaster-bearing body in the disaster causing range, and evaluating vulnerability and value of the disaster-bearing body according to basic information and structural characteristics of the disaster-bearing body;
and constructing a risk factor set according to the occurrence probability of the geological disasters, the arrival disaster-bearing body probability, the disaster-bearing body space-time probability, the vulnerability of the disaster-bearing body and the disaster-bearing body value, and evaluating the geological disaster risk of the target area based on the risk factor set by using an annual risk mode.
2. The geological disaster risk evaluation method based on the multi-element feature fusion according to claim 1, wherein the preprocessing of the multi-source monitoring data is performed to obtain the occurrence probability of potential geological disasters, and the occurrence probability of the geological disasters is generated specifically as follows:
acquiring multisource monitoring data of a target area, performing data cleaning on the retrieved multisource monitoring data, and screening precipitation data and rock-soil structural characteristics from the preprocessed multisource monitoring data;
accessing a related geological disaster database, establishing a retrieval tag according to GIS data corresponding to a target area, acquiring geological disaster examples in the target area, and reading the daily maximum precipitation under different preset reproduction periods by adopting a generalized extremum distribution function based on precipitation data;
extracting precipitation critical values corresponding to different geological disaster stable states and unstable states in the geological disaster example, acquiring an accumulated probability distribution function according to the precipitation critical values, and predicting the occurrence probability of the daily maximum precipitation reproduction period by using the accumulated probability distribution function of the precipitation critical values as the rainfall induction probability;
clustering according to the rock-soil structural features of different geological disaster examples, obtaining geological disaster example clusters corresponding to the different rock-soil structural features, judging example data amounts of the different clusters, and eliminating the geological disaster example clusters which do not meet the preset data amount standard;
performing disaster space-time evolution analysis in the screened geological disaster instance clusters, extracting dominant factor sets of different geological disaster instance clusters, and performing stability factor selection in the dominant factor sets by using a Fisher algorithm;
judging whether pearson correlation coefficients of different dominant factors and stability meet preset requirements or not by taking the maximum geological disaster recognition accuracy as a target, and acquiring dominant factors meeting the preset requirements as factors influencing the stability of the rock-soil body;
and obtaining geological disaster instability probabilities of different rock-soil structure areas in the target area by utilizing a Monte Carlo method through factors influencing the stability of the rock-soil body, and obtaining the occurrence probability of the geological disaster by combining the geological disaster instability probabilities with the rainfall induction probability.
3. The geological disaster risk evaluation method based on the multi-element feature fusion according to claim 1, wherein the judging of the probability of reaching the disaster-bearing body corresponding to the geological disaster is specifically as follows:
acquiring a rock-soil structure characteristic of a position where a hidden trouble point is located, carrying out initial hidden trouble assessment based on the rock-soil structure characteristic, determining a neighborhood region range corresponding to the hidden trouble point, and carrying out gridding treatment on the neighborhood region range;
extracting geographic conditions of hidden danger points through geological survey data of a target region, performing similarity calculation in a neighborhood grid according to the geographic conditions, and selecting the neighborhood grid meeting a preset similarity standard for marking to serve as a related hidden danger point;
acquiring hidden danger points and the regional range of the grid where the hidden danger points are located and the rock-soil structural characteristics of the regional range as hidden danger characteristics, and screening similar geological disaster examples meeting corresponding standards from geological disaster examples by utilizing the hidden danger characteristics;
and carrying out integration analysis according to the similar geological disaster examples to obtain a motion path and a sliding distance of the landslide of the hidden danger point, determining the position information of the disaster-affected object in the target area, and obtaining the probability of the geological disaster reaching the disaster-bearing body according to the structural characteristics of the side slope of the hidden danger point by adopting a sliding distance method.
4. The geological disaster risk evaluation method based on the multi-element feature fusion according to claim 1, wherein a disaster causing range of a geological disaster is obtained, and space-time probability of a disaster bearing body is obtained according to time of the disaster bearing body in the disaster causing range, specifically:
determining a disaster causing range of the geological disaster according to the movement path and the sliding distance of the geological disaster at the hidden danger point, and dividing a disaster bearing body in the target area into a fixed disaster bearing body and a movable disaster bearing body according to whether the disaster bearing body can move or not;
acquiring a threat range of a geological disaster at a hidden trouble point by utilizing the disaster causing range, judging whether the fixed disaster bearing body is in the threat range, and acquiring space-time probability of the fixed disaster bearing body according to a judging result;
and acquiring natural properties of landslide bodies corresponding to the geological disasters at hidden danger points, early warning signals, evacuation systems of target areas and the movement capacities of disaster-bearing bodies and personnel as adjustment parameters, and calculating the space-time probability of the movable disaster-bearing bodies according to the time of the movable disaster-bearing bodies in the disaster-causing range.
5. The geological disaster risk evaluation method based on the multi-element feature fusion according to claim 1, wherein the vulnerability and the value of the disaster-bearing body are evaluated according to the basic information and the structural features of the disaster-bearing body, specifically:
acquiring vulnerability evaluation indexes according to self-inherent properties of disaster-bearing bodies to construct a vulnerability evaluation model, wherein the vulnerability evaluation model is expressed as，/>Is vulnerability of disaster-bearing bodies; />Is an index of vulnerability;
taking the structure type, deformation condition and service life as building vulnerability evaluation indexes, and taking the personnel physical condition, personnel age structure and geological disaster cognition level as personnel vulnerability evaluation indexes;
information expansion is carried out in a preset search space through data search by utilizing vulnerability evaluation indexes of buildings and personnel, descriptive texts of the evaluation indexes are obtained, word segmentation is carried out on the descriptive texts to obtain word vectors, and descriptive keywords are obtained according to occurrence frequency of the word vectors;
generating text vectors of descriptive keywords based on context through Bert training, simplifying descriptive texts according to the text vectors, and obtaining descriptions and index values of vulnerability evaluation indexes;
and estimating the value of the disaster-bearing body of the building and the traffic route according to the structure, the area and the length, and acquiring the value loss of the disaster-bearing body by utilizing the ratio of the value of the disaster-bearing body suffering from disaster damage loss to the value of the disaster-bearing body before disaster.
6. The geological disaster risk evaluation method based on the multi-element feature fusion according to claim 1, wherein the geological disaster risk evaluation of the target area is performed based on the risk factor set by using an annual risk mode, specifically comprising the following steps:
calculating property annual loss and personnel annual death probability according to the occurrence probability of geological disasters, the probability of reaching a disaster-bearing body, the space-time probability of the disaster-bearing body, the vulnerability of the disaster-bearing body and the value of the disaster-bearing body as risk factors;
the loss of property for years is expressed as；
The probability of death of a person over the years is expressed as；
Wherein,for property annual loss, < >>For the single annual death probability->For the annual probability of occurrence of a geological disaster,for the probability of geological disasters reaching disaster-bearing bodies, < +.>Space-time probability for a fixed or mobile disaster-bearing body, < + >>Is easy to wear and tear on property>For disaster tolerance, the person is treated with->Is vulnerable to personnel.
7. Geological disaster risk evaluation system based on multi-element feature fusion, which is characterized by comprising: the device comprises a memory and a processor, wherein the memory comprises a geological disaster risk evaluation method program based on multi-element feature fusion, and the geological disaster risk evaluation method program based on multi-element feature fusion realizes the following steps when being executed by the processor:
the method comprises the steps of obtaining multi-source monitoring data of a target area, preprocessing the multi-source monitoring data to obtain occurrence probability of potential geological disasters, generating occurrence probability of the geological disasters, and screening hidden danger points according to the occurrence probability of the geological disasters;
analyzing the movement path and the sliding distance of the geological disaster according to the geographical condition of the position of the hidden danger point, and judging the probability of reaching the disaster-bearing body corresponding to the geological disaster based on the sliding distance and the geographical information of the dangerous object;
acquiring a disaster causing range of a geological disaster, acquiring space-time probability of a disaster-bearing body according to time of the disaster-bearing body in the disaster causing range, and evaluating vulnerability and value of the disaster-bearing body according to basic information and structural characteristics of the disaster-bearing body;
and constructing a risk factor set according to the occurrence probability of the geological disasters, the arrival disaster-bearing body probability, the disaster-bearing body space-time probability, the vulnerability of the disaster-bearing body and the disaster-bearing body value, and evaluating the geological disaster risk of the target area based on the risk factor set by using an annual risk mode.
8. The geological disaster risk evaluation system based on the multi-element feature fusion according to claim 7, wherein the geological disaster risk evaluation of the target area is performed based on the risk factor set by using an annual risk mode, specifically:
calculating property annual loss and personnel annual death probability according to the occurrence probability of geological disasters, the probability of reaching a disaster-bearing body, the space-time probability of the disaster-bearing body, the vulnerability of the disaster-bearing body and the value of the disaster-bearing body as risk factors;
the loss of property for years is expressed as；
The probability of death of a person over the years is expressed as；
Wherein,is loss of property，/>For the single annual death probability->For the annual probability of occurrence of a geological disaster,for the probability of geological disasters reaching disaster-bearing bodies, < +.>Space-time probability for a fixed or mobile disaster-bearing body, < + >>Is easy to wear and tear on property>For disaster tolerance, the person is treated with->Is vulnerable to personnel.
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