CN115311821B - Geological disaster intelligent monitoring cloud platform based on digital twin technology - Google Patents
Geological disaster intelligent monitoring cloud platform based on digital twin technology Download PDFInfo
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Abstract
The invention discloses a digital twin technology-based intelligent geological disaster monitoring cloud platform, which is characterized in that geological data of each target mountain slope is obtained in real time, disaster risk coefficients of each target mountain slope are obtained through analysis, whether each target mountain slope has disaster risk or not is judged, geological hidden danger coefficients of each target mountain slope stored in a geological information base of the target mountain slope are further extracted, meanwhile, meteorological information of each target mountain slope is obtained through analysis, meteorological influence coefficients of each target mountain slope are obtained through analysis, disaster prediction indexes of each target mountain slope are obtained through evaluation according to the geological hidden danger coefficients and the meteorological influence coefficients of each target mountain slope, and corresponding processing is carried out, so that defects of the traditional mountain landslide monitoring technology are overcome, reliability, accuracy and timeliness of monitoring results are greatly improved, reliable decision basis is provided for geological disaster engineering safety management, and occurrence of malignant geological disasters is avoided.
Description
Technical Field
The invention relates to the field of landslide monitoring and analysis, in particular to a geological disaster intelligent monitoring cloud platform based on a digital twin technology.
Background
Along with the continuous development of social economy, the resource development strength is gradually increased, great influence is caused to the fragile geological environment, the frequency and the scale of geological disasters are gradually increased, national economic construction and life and property safety of people are seriously influenced, geological disaster monitoring is more and more important as an important means for preventing and controlling the ground disaster, landslide is common in the geological disasters, and therefore monitoring and early warning on the landslide is more important.
The traditional landslide monitoring technology has some defects: on the one hand, traditional landslide monitoring adopts manual monitoring, is limited by environmental characteristics, and work efficiency is low, input is big, monitoring cost and security risk are all higher, and on the other hand, traditional landslide monitoring's analysis index is single, does not comprehensively consider the influence of mountain self topography factor and meteorological factor, and then makes the reliability and the accuracy of monitoring result all lower.
Disclosure of Invention
Aiming at the problems, the invention provides a geological disaster intelligent monitoring cloud platform based on a digital twin technology, which realizes the function of monitoring and analyzing landslide.
The technical scheme adopted for solving the technical problems is as follows:
the invention provides a geological disaster intelligent monitoring cloud platform based on a digital twin technology, which comprises the following components:
the target hillside geological data acquisition module: the method comprises the steps of acquiring geological data of each target hillside in real time, wherein the geological data comprise the appearance deformation of the hillside, the deep horizontal displacement variation of the soil body and the groundwater level variation;
the target hillside geological data analysis module: the system comprises a target hillside geological information base, a target hillside analysis module and a target hillside analysis module, wherein the target hillside analysis module is used for analyzing and obtaining disaster risk coefficients of each target hillside according to geological data of each target hillside, judging whether each target hillside has disaster risk, if so, early warning is carried out, otherwise, the geological data of the target hillside is stored in the target hillside geological information base;
target hillside geological information base: the system is used for receiving and storing geological data of each target hillside, and simultaneously storing initial three-dimensional coordinates of each appearance deformation monitoring point in each target hillside, initial horizontal displacement of each deep horizontal displacement monitoring point in each target hillside and initial ground water level height of each ground water level monitoring point in each target hillside;
the target hillside geological hidden danger coefficient acquisition module comprises: the method comprises the steps of extracting geological data of each target hillside stored in a target hillside geological information base, and analyzing to obtain geological hidden danger coefficients of each target hillside;
the target hillside weather influence coefficient acquisition module is used for: the method comprises the steps of acquiring weather information of each target hillside, and analyzing to obtain weather influence coefficients of each target hillside;
the target hillside disaster prediction evaluation module is used for: and the disaster prediction index of each target hillside is obtained by evaluation according to the geological hidden danger coefficient and the meteorological influence coefficient of each target hillside, and corresponding processing is carried out.
On the basis of the above embodiment, the specific process of the target hillside geological data acquisition module includes:
extracting initial three-dimensional coordinates of each appearance deformation monitoring point in each target hillside stored in the target hillside geological information base, and marking the initial three-dimensional coordinates asi denotes the number of the i-th target hillside, i=1, 2,..n, p denotes the number of the p-th appearance deformation monitoring point, p=1, 2,..q;
three-dimensional coordinates of appearance deformation monitoring points in each target hillside are obtained in real time through a GNSS positioning technology and are recorded as (X) ip ,Y ip ,Z ip )；
Substituting three-dimensional coordinates of appearance deformation monitoring points in each target hillside into a formulaObtaining the slope appearance deformation quantity alpha of each target hillside i Wherein q represents the number of appearance deformation monitoring points, and beta represents a preset slope appearance deformation amount correction factor of the target slope.
On the basis of the above embodiment, the specific process in the target hillside geological data acquisition module further includes:
extracting initial horizontal displacement of each deep horizontal displacement monitoring point in each target hillside stored in the target hillside geological information base, and marking the initial horizontal displacement asp 'represents the number of the p' th deep horizontal displacement monitoring point, p '=1', 2',..q';
the horizontal displacement of each deep horizontal displacement monitoring point in each target hillside is measured in real time through a sliding inclinometer and is marked as b ip′ Substituting the horizontal displacement of each deep horizontal displacement monitoring point in each target hillside into a formulaObtaining the deep soil horizontal displacement variation χ of each target hillside i Wherein q' represents the number of deep horizontal displacement monitoring points, and delta represents a preset soil deep horizontal displacement variation correction factor of the target hillside.
On the basis of the above embodiment, the specific process in the target hillside geological data acquisition module further includes:
extracting target mountainThe initial ground water level height of each ground water level monitoring point in each target mountain slope stored in the slope geological information base is recorded asp "represents the number of the p" th groundwater level monitoring point, p "=1", 2 ",..q";
the water level sensor detects the water level height of each water level monitoring point in each target hillside in real time, marks the water level height as a hip ", and substitutes the water level height of each water level monitoring point in each target hillside into a formulaObtaining the ground water level variation epsilon of each target hillside i Wherein q' represents the number of ground water level monitoring points, and phi represents a ground water level variation correction factor of a preset target hillside.
Based on the above embodiment, the specific analysis process in the target hillside geological data analysis module is as follows:
slope appearance variable alpha of each target hillside i Deep horizontal displacement variable χ of soil body i And the ground water level variation epsilon i Substitution into formula z i ＝τ 1 *α i +τ 2 *χ i +τ 3 *ε i Obtaining disaster risk coefficient z of each target hillside i Wherein τ 1 、τ 2 、τ 3 Respectively representing the preset weight factors of the slope appearance deformation quantity, the soil deep horizontal displacement change quantity and the groundwater level change quantity of the target hillside;
comparing the disaster risk coefficient of each target mountain slope with a preset disaster risk coefficient threshold value of the mountain slope, if the disaster risk coefficient of a certain target mountain slope is larger than or equal to the preset disaster risk coefficient threshold value of the mountain slope, then the target mountain slope has disaster risk, and sending the serial number of the target mountain slope to a disaster monitoring cloud platform for early warning, otherwise, the target mountain slope temporarily has no disaster risk, and the geological data of the target mountain slope are stored in a target mountain slope geological information base.
On the basis of the above embodiment, the geological hidden danger coefficient obtaining module of the target hillside analyzes and obtains the geological hidden danger coefficient of each target hillside, and the specific process is as follows:
extracting geological data of each target hillside stored in a target hillside geological information base to obtain three-dimensional coordinates of each appearance deformation monitoring point in each target hillside at each sampling time in a preset monitoring history period, horizontal displacement of each deep horizontal displacement monitoring point in each target hillside at each sampling time in the preset monitoring history period and ground water level height of each ground water level monitoring point in each target hillside at each sampling time in the preset monitoring history period, and respectively marking the three-dimensional coordinates as ground water level heights of each ground water level monitoring point in each target hillside at each sampling time in the preset monitoring history periodAnd->j represents the number of the j sampling moment, j=1, 2,..m, further analyzing to obtain the slope appearance deformation quantity, the soil deep level horizontal displacement change quantity and the groundwater level change quantity of each target hillside at each sampling moment in a preset monitoring history period, and respectively marking the slope appearance deformation quantity, the soil deep level horizontal displacement change quantity and the groundwater level change quantity as +.>And->
Slope appearance deformation of each target slope at each sampling moment in a preset monitoring history periodDeep horizontal displacement variable quantity of soil body>And groundwater level variation->Substitution formulaObtaining the geological hidden trouble coefficient gamma of each target hillside i Wherein m represents the number of acquisition instants, +.>Indicating the slope appearance deformation quantity of the ith target slope at the j-1 th sampling time in the preset monitoring history period,/for>Representing the change quantity of the deep horizontal displacement of the soil body at the j-1 th sampling moment of the ith target hillside in a preset monitoring history period,/for>The change quantity of the groundwater level of the ith target hillside at the j-1 th sampling moment in the preset monitoring history period is represented, and eta represents the geological hidden trouble coefficient correction factor of the preset target hillside.
On the basis of the above embodiment, the weather influence coefficient obtaining module of the target hillside obtains the weather influence coefficient of each target hillside by analysis, and the specific process is as follows:
the rainfall of each rainfall monitoring point in each target hillside is detected through a rainfall gauge and is marked as f ic C represents the number of the c-th rainfall monitoring point, c=1, 2, d;
detecting the air temperature of each air temperature monitoring point in each target hillside by a hygrometer, and marking the air temperature as g iu U represents the number of the u-th air temperature monitoring point, u=1, 2, v;
substituting the rainfall of each rainfall monitoring point in each target hillside and the air temperature of each air temperature monitoring point in each target hillside into a formulaObtaining weather influence coefficient kappa of each target hillside i Wherein f Is provided with G represents a preset target hillside rainfall threshold value Is provided with Representing a preset target hillside air temperature reference value, delta g Is provided with Indicating a preset target hill air temperature allowable error lambda 1 、λ 2 Respectively representing the weight factors of the rainfall and the air temperature of the preset target hillside.
Based on the above embodiment, the specific analysis process of the target hillside disaster prediction and assessment module is as follows:
the geological hidden trouble coefficient gamma of each target hillside i And weather influence coefficient K of each target hillside i Substitution formulaObtaining disaster prediction index xi of each target hillside i Wherein ψ represents a compensation factor of weather influencing coefficients of a preset target hillside, +.>A disaster prediction index correction factor of a preset target hillside is represented, and e represents a natural constant;
comparing the disaster prediction index of each target mountain slope with a preset disaster prediction index threshold of the target mountain slope, if the disaster prediction index of a certain target mountain slope is larger than the disaster prediction index threshold of the preset target mountain slope, marking the target mountain slope as a predicted disaster risk mountain slope, counting to obtain the serial numbers of each predicted disaster risk mountain slope, and transmitting the serial numbers of each predicted disaster risk mountain slope to a disaster monitoring cloud platform.
Compared with the prior art, the intelligent geological disaster monitoring cloud platform based on the digital twin technology has the following beneficial effects:
according to the intelligent monitoring cloud platform for the geological disasters based on the digital twin technology, provided by the invention, the slope appearance deformation quantity, the soil deep level displacement change quantity and the underground water level change quantity of each target mountain slope are obtained in real time, the disaster risk coefficient of each target mountain slope is obtained through analysis, whether each target mountain slope has disaster risk or not is judged, if a certain target mountain slope has disaster risk, early warning is carried out, otherwise, the geological data of the target mountain slope are stored in the geological information base of the target mountain slope, further, the geological hidden danger coefficient of each target mountain slope is obtained through analysis, meanwhile, the meteorological information of each target mountain slope is obtained, the meteorological influence coefficient of each target mountain slope is obtained through analysis, the disaster prediction index of each target mountain slope is obtained through evaluation according to the geological hidden danger coefficient and the meteorological influence coefficient of each target mountain slope, the disaster prediction index of each target mountain slope is processed correspondingly, the defects of the traditional mountain landslide monitoring technology are overcome, the working efficiency is improved, the monitoring cost and the safety risk are reduced, the geological data and the geological image data are combined to carry out full-aspect monitoring analysis on the target mountain slope, further, the geological decision and the reliability is greatly improved, the reliability of the monitoring is improved, the serious disaster is avoided, and the reliability is ensured, and the serious disaster safety and the disaster is avoided.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram illustrating a system module connection according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a digital twin technology-based intelligent geological disaster monitoring cloud platform, which comprises a target hillside geological data acquisition module, a target hillside geological data analysis module, a target hillside geological information base, a target hillside geological hidden danger coefficient acquisition module, a target hillside weather influence coefficient acquisition module and a target hillside disaster prediction evaluation module.
The target hillside geological data analysis module is connected with the target hillside geological data acquisition module, the target hillside disaster prediction evaluation module is respectively connected with the target hillside geological hidden danger coefficient acquisition module and the target hillside weather influence coefficient acquisition module, and the target hillside geological information base is respectively connected with the target hillside geological data acquisition module, the target hillside geological data analysis module and the target hillside geological hidden danger coefficient acquisition module.
The target hillside geological data acquisition module is used for acquiring geological data of each target hillside in real time, wherein the geological data comprise slope appearance deformation, soil deep horizontal displacement change and groundwater level change.
Further, the specific process of the target hillside geological data acquisition module comprises the following steps:
extracting initial three-dimensional coordinates of each appearance deformation monitoring point in each target hillside stored in the target hillside geological information base, and marking the initial three-dimensional coordinates asi denotes the number of the i-th target hillside, i=1, 2,..n, p denotes the number of the p-th appearance deformation monitoring point, p=1, 2,..q;
three-dimensional coordinates of appearance deformation monitoring points in each target hillside are obtained in real time through a GNSS positioning technology and are recorded as (X) ip ,Y ip ,Z ip )；
Substituting three-dimensional coordinates of appearance deformation monitoring points in each target hillside into a formulaObtaining the slope appearance deformation quantity alpha of each target hillside i Wherein q represents the number of appearance deformation monitoring points, and beta represents a preset slope appearance deformation amount correction factor of the target slope.
Further, the specific process in the target hillside geological data acquisition module further comprises:
extracting each depth in each target hillside stored in the target hillside geological information baseInitial horizontal displacement of the layer horizontal displacement monitoring point is recorded asp 'represents the number of the p' th deep horizontal displacement monitoring point, p '=1', 2',..q';
the horizontal displacement of each deep horizontal displacement monitoring point in each target hillside is measured in real time through a sliding inclinometer and is marked as b ip′ Substituting the horizontal displacement of each deep horizontal displacement monitoring point in each target hillside into a formulaObtaining the deep soil horizontal displacement variation χ of each target hillside i Wherein q' represents the number of deep horizontal displacement monitoring points, and delta represents a preset soil deep horizontal displacement variation correction factor of the target hillside.
Further, the specific process in the target hillside geological data acquisition module further comprises:
extracting the initial ground water level height of each ground water level monitoring point in each target hillside stored in the target hillside geological information base, and marking the initial ground water level height asp "represents the number of the p" th groundwater level monitoring point, p "=1", 2 ",..q";
the water level sensor detects the water level height of each water level monitoring point in each target hillside in real time, marks the water level height as a hip ", and substitutes the water level height of each water level monitoring point in each target hillside into a formulaObtaining the ground water level variation epsilon of each target hillside i Wherein q' represents the number of ground water level monitoring points, and phi represents a ground water level variation correction factor of a preset target hillside.
The target hillside geological data analysis module is used for analyzing and obtaining disaster risk coefficients of each target hillside according to geological data of each target hillside, judging whether each target hillside has disaster risk, if so, carrying out early warning, otherwise, storing the geological data of the target hillside into the target hillside geological information base.
Further, the specific analysis process in the target hillside geological data analysis module is as follows:
slope appearance variable alpha of each target hillside i Deep horizontal displacement variable χ of soil body i And the ground water level variation epsilon i Substitution into formula z i ＝τ 1 *α i +τ 2 *χ i +τ 3 *ε i Obtaining disaster risk coefficient z of each target hillside i Wherein τ 1 、τ 2 、τ 3 Respectively representing the preset weight factors of the slope appearance deformation quantity, the soil deep horizontal displacement change quantity and the groundwater level change quantity of the target hillside;
comparing the disaster risk coefficient of each target mountain slope with a preset disaster risk coefficient threshold value of the mountain slope, if the disaster risk coefficient of a certain target mountain slope is larger than or equal to the preset disaster risk coefficient threshold value of the mountain slope, then the target mountain slope has disaster risk, and sending the serial number of the target mountain slope to a disaster monitoring cloud platform for early warning, otherwise, the target mountain slope temporarily has no disaster risk, and the geological data of the target mountain slope are stored in a target mountain slope geological information base.
The invention obtains the slope appearance deformation quantity, the soil deep level displacement change quantity and the underground water level change quantity of each target mountain slope in real time, analyzes to obtain the disaster risk coefficient of each target mountain slope, judges whether each target mountain slope has disaster risk, if so, performs early warning, otherwise, stores the geological data of the target mountain slope into the target mountain slope geological information base, analyzes the geological condition of the target mountain slope from multiple dimensions, monitors the change of the geological data of the target mountain slope in real time, and greatly improves the accuracy and timeliness of the geological monitoring result of the target mountain slope.
The target hillside geological information base is used for receiving and storing geological data of each target hillside, and simultaneously storing initial three-dimensional coordinates of each appearance deformation monitoring point in each target hillside, initial horizontal displacement of each deep horizontal displacement monitoring point in each target hillside and initial ground water level height of each ground water level monitoring point in each target hillside.
The target hillside geological hidden danger coefficient acquisition module is used for extracting geological data of each target hillside stored in the target hillside geological information base and analyzing to obtain geological hidden danger coefficients of each target hillside.
Further, the geological hidden danger coefficient of each target hillside is obtained by analysis in the geological hidden danger coefficient obtaining module of the target hillside, and the specific process is as follows:
extracting geological data of each target hillside stored in a target hillside geological information base to obtain three-dimensional coordinates of each appearance deformation monitoring point in each target hillside at each sampling time in a preset monitoring history period, horizontal displacement of each deep horizontal displacement monitoring point in each target hillside at each sampling time in the preset monitoring history period and ground water level height of each ground water level monitoring point in each target hillside at each sampling time in the preset monitoring history period, and respectively marking the three-dimensional coordinates as ground water level heights of each ground water level monitoring point in each target hillside at each sampling time in the preset monitoring history periodAnd->j represents the number of the j sampling moment, j=1, 2,..m, further analyzing to obtain the slope appearance deformation quantity, the soil deep level horizontal displacement change quantity and the groundwater level change quantity of each target hillside at each sampling moment in a preset monitoring history period, and respectively marking the slope appearance deformation quantity, the soil deep level horizontal displacement change quantity and the groundwater level change quantity as +.>And->
Slope appearance deformation of each target slope at each sampling moment in a preset monitoring history periodDeep horizontal displacement variable quantity of soil body>And groundwater level variation->Substitution formulaObtaining the geological hidden trouble coefficient gamma of each target hillside i Wherein m represents the number of acquisition instants, +.>Indicating the slope appearance deformation quantity of the ith target slope at the j-1 th sampling time in the preset monitoring history period,/for>Representing the change quantity of the deep horizontal displacement of the soil body at the j-1 th sampling moment of the ith target hillside in a preset monitoring history period,/for>The change quantity of the groundwater level of the ith target hillside at the j-1 th sampling moment in the preset monitoring history period is represented, and eta represents the geological hidden trouble coefficient correction factor of the preset target hillside.
As a preferable scheme, the specific analysis method of the slope appearance deformation quantity, the soil deep level horizontal displacement change quantity and the groundwater level change quantity of each target hillside at each sampling moment in the preset monitoring history period comprises the following steps:
substituting three-dimensional coordinates of each appearance deformation monitoring point in each target hillside in each sampling moment in a preset monitoring history period into a formulaObtaining the preset monitoring history of each target hillsideSlope appearance variable +.>
Substituting the horizontal displacement of each deep horizontal displacement monitoring point in each target hillside in each sampling moment in a preset monitoring history period into a formulaObtaining the deep soil horizontal displacement variation of each target hillside at each sampling moment in a preset monitoring history period>
Substituting the ground water level height of each ground water level monitoring point in each target hillside at each sampling moment in a preset monitoring history period into a formulaObtaining the underground water level variation of each target hillside at each sampling moment in a preset monitoring history period>
The target hillside weather influence coefficient acquisition module is used for acquiring weather information of each target hillside and analyzing to obtain weather influence coefficients of each target hillside.
Further, the weather influence coefficients of the target hillsides are obtained by analysis in the target hillside weather influence coefficient obtaining module, and the specific process is as follows:
the rainfall of each rainfall monitoring point in each target hillside is detected through a rainfall gauge and is marked as f ic C represents the number of the c-th rainfall monitoring point, c=1, 2, d;
detecting the air temperature of each air temperature monitoring point in each target hillside by a hygrometer, and marking the air temperature as g iu U represents the number of the u-th air temperature monitoring point, u=1, 2, v;
monitoring each rainfall in each target hillsideRainfall of points and air temperature substitution formula of air temperature monitoring points in each target hillsideObtaining weather influence coefficient kappa of each target hillside i Wherein f Is provided with G represents a preset target hillside rainfall threshold value Is provided with Representing a preset target hillside air temperature reference value, delta g Is provided with Indicating a preset target hill air temperature allowable error lambda 1 、λ 2 Respectively representing the weight factors of the rainfall and the air temperature of the preset target hillside.
The target hillside disaster prediction evaluation module is used for evaluating and obtaining disaster prediction indexes of each target hillside according to geological hidden danger coefficients and meteorological influence coefficients of each target hillside and performing corresponding processing.
Further, the specific analysis process of the target hillside disaster prediction and evaluation module is as follows:
the geological hidden trouble coefficient gamma of each target hillside i And weather influence coefficient K of each target hillside i Substitution formulaObtaining disaster prediction index xi of each target hillside i Wherein ψ represents a compensation factor of weather influencing coefficients of a preset target hillside, +.>A disaster prediction index correction factor of a preset target hillside is represented, and e represents a natural constant;
comparing the disaster prediction index of each target mountain slope with a preset disaster prediction index threshold of the target mountain slope, if the disaster prediction index of a certain target mountain slope is larger than the disaster prediction index threshold of the preset target mountain slope, marking the target mountain slope as a predicted disaster risk mountain slope, counting to obtain the serial numbers of each predicted disaster risk mountain slope, and transmitting the serial numbers of each predicted disaster risk mountain slope to a disaster monitoring cloud platform.
The invention extracts the geological data of each target mountain slope stored in the geological information base of the target mountain slope, analyzes to obtain the geological hidden danger coefficient of each target mountain slope, acquires the meteorological information of each target mountain slope, analyzes to obtain the meteorological influence coefficient of each target mountain slope, evaluates and obtains the disaster prediction index of each target mountain slope according to the geological hidden danger coefficient and the meteorological influence coefficient of each target mountain slope, carries out corresponding processing, overcomes the defects of the traditional mountain landslide monitoring technology, improves the working efficiency, reduces the monitoring cost and the safety risk, carries out the comprehensive monitoring analysis on the target mountain slope by combining the geological data and the meteorological data, and further greatly improves the reliability and the accuracy of the monitoring result, thereby providing a reliable decision basis for the safety management measures of geological disaster engineering and avoiding the occurrence of malignant geological disasters.
The foregoing is merely illustrative and explanatory of the principles of the invention, as various modifications and additions may be made to the specific embodiments described, or similar thereto, by those skilled in the art, without departing from the principles of the invention or beyond the scope of the appended claims.
Claims (5)
1. Geological disaster intelligent monitoring cloud platform based on digital twin technique, characterized by comprising:
the target hillside geological data acquisition module: the method comprises the steps of acquiring geological data of each target hillside in real time, wherein the geological data comprise the appearance deformation of the hillside, the deep horizontal displacement variation of the soil body and the groundwater level variation;
the target hillside geological data analysis module: the system comprises a target hillside geological information base, a target hillside analysis module and a target hillside analysis module, wherein the target hillside analysis module is used for analyzing and obtaining disaster risk coefficients of each target hillside according to geological data of each target hillside, judging whether each target hillside has disaster risk, if so, early warning is carried out, otherwise, the geological data of the target hillside is stored in the target hillside geological information base;
target hillside geological information base: the system is used for receiving and storing geological data of each target hillside, and simultaneously storing initial three-dimensional coordinates of each appearance deformation monitoring point in each target hillside, initial horizontal displacement of each deep horizontal displacement monitoring point in each target hillside and initial ground water level height of each ground water level monitoring point in each target hillside;
the target hillside geological hidden danger coefficient acquisition module comprises: the method comprises the steps of extracting geological data of each target hillside stored in a target hillside geological information base, and analyzing to obtain geological hidden danger coefficients of each target hillside;
the geological hidden danger coefficient of each target hillside is obtained by analysis in the geological hidden danger coefficient obtaining module of the target hillside, and the specific process is as follows:
extracting geological data of each target hillside stored in a target hillside geological information base to obtain three-dimensional coordinates of each appearance deformation monitoring point in each target hillside at each sampling time in a preset monitoring history period, horizontal displacement of each deep horizontal displacement monitoring point in each target hillside at each sampling time in the preset monitoring history period and ground water level height of each ground water level monitoring point in each target hillside at each sampling time in the preset monitoring history period, and respectively marking the three-dimensional coordinates as ground water level heights of each ground water level monitoring point in each target hillside at each sampling time in the preset monitoring history period And->i represents the number of the ith target hillside, i=1, 2,..n, p represents the number of the p-th appearance deformation monitoring point, p=1, 2,.. q j represents the number of the j-th sampling time, j=1, 2,..m, and the slope appearance deformation amount, soil deep horizontal displacement change amount and ground water level change amount of each target hillside at each sampling time in the preset monitoring history period are further analyzed and obtained and respectively recorded as +>And->
Slope appearance deformation of each target slope at each sampling moment in a preset monitoring history periodDeep horizontal displacement variable quantity of soil body>And groundwater level variation->Substitution formula
Obtaining the geological hidden trouble coefficient gamma of each target hillside i Wherein m represents the number of acquisition instants, +.>Indicating the slope appearance deformation quantity of the ith target slope at the j-1 th sampling time in the preset monitoring history period,/for>Representing the change quantity of the deep horizontal displacement of the soil body at the j-1 th sampling moment of the ith target hillside in a preset monitoring history period,/for>Representing the groundwater level variation quantity of the ith target hillside at the j-1 th sampling moment in a preset monitoring history period, wherein eta represents a geological hidden danger coefficient correction factor of the preset target hillside;
the target hillside weather influence coefficient acquisition module is used for: the method comprises the steps of acquiring weather information of each target hillside, and analyzing to obtain weather influence coefficients of each target hillside;
the weather influence coefficients of the target hillsides are obtained by analysis in the target hillside weather influence coefficient obtaining module, and the specific process is as follows:
the rainfall of each rainfall monitoring point in each target hillside is detected through a rainfall gauge and is marked as f ic C represents the number of the c-th rainfall monitoring point, c=1, 2, d;
detecting the air temperature of each air temperature monitoring point in each target hillside by a hygrometer, and marking the air temperature as g iu U represents the number of the u-th air temperature monitoring point, u=1, 2, v;
substituting the rainfall of each rainfall monitoring point in each target hillside and the air temperature of each air temperature monitoring point in each target hillside into a formulaObtaining weather influence coefficient kappa of each target hillside i Wherein f Is provided with G represents a preset target hillside rainfall threshold value Is provided with Representing a preset target hillside air temperature reference value, delta g Is provided with Indicating a preset target hill air temperature allowable error lambda 1 、λ 2 Respectively representing weight factors of the rainfall and the air temperature of a preset target hillside;
the target hillside disaster prediction evaluation module is used for: the disaster prediction index of each target hillside is obtained through evaluation according to the geological hidden danger coefficient and the meteorological influence coefficient of each target hillside, and corresponding processing is carried out;
the specific analysis process of the target hillside disaster prediction evaluation module is as follows:
the geological hidden trouble coefficient gamma of each target hillside i And weather influence coefficient K of each target hillside i Substitution formulaObtaining disaster prediction index xi of each target hillside i Wherein ζ represents a compensation factor of a weather influence coefficient of a preset target hillside, ζ represents a disaster prediction index correction factor of the preset target hillside, and e represents a natural constant;
comparing the disaster prediction index of each target mountain slope with a preset disaster prediction index threshold of the target mountain slope, if the disaster prediction index of a certain target mountain slope is larger than the disaster prediction index threshold of the preset target mountain slope, marking the target mountain slope as a predicted disaster risk mountain slope, counting to obtain the serial numbers of each predicted disaster risk mountain slope, and transmitting the serial numbers of each predicted disaster risk mountain slope to a disaster monitoring cloud platform.
2. The intelligent geological disaster monitoring cloud platform based on the digital twin technology as set forth in claim 1, wherein: the specific process of the target hillside geological data acquisition module comprises the following steps:
extracting initial three-dimensional coordinates of each appearance deformation monitoring point in each target hillside stored in the target hillside geological information base, and marking the initial three-dimensional coordinates as
Three-dimensional coordinates of appearance deformation monitoring points in each target hillside are obtained in real time through a GNSS positioning technology and are recorded as (X) ip ,Y ip ,Z ip )；
Substituting three-dimensional coordinates of appearance deformation monitoring points in each target hillside into a formulaObtaining the slope appearance deformation quantity alpha of each target hillside i Wherein q represents the number of appearance deformation monitoring points, and beta represents a preset slope appearance deformation amount correction factor of the target slope.
3. The intelligent geological disaster monitoring cloud platform based on the digital twin technology as set forth in claim 1, wherein: the specific process in the target hillside geological data acquisition module further comprises the following steps:
extracting initial horizontal displacement of each deep horizontal displacement monitoring point in each target hillside stored in the target hillside geological information base, and marking the initial horizontal displacement asp 'represents the number of the p' th deep horizontal displacement monitoring point, p '=1', 2',..q';
the horizontal displacement of each deep horizontal displacement monitoring point in each target hillside is measured in real time through a sliding inclinometer and is marked as b ip′ Substituting the horizontal displacement of each deep horizontal displacement monitoring point in each target hillside into a formulaObtaining the deep soil horizontal displacement variation χ of each target hillside i Wherein q' represents the number of deep horizontal displacement monitoring points, and delta represents a preset soil deep horizontal displacement variation correction factor of the target hillside.
4. The intelligent geological disaster monitoring cloud platform based on the digital twin technology as set forth in claim 1, wherein: the specific process in the target hillside geological data acquisition module further comprises the following steps:
extracting the initial ground water level height of each ground water level monitoring point in each target hillside stored in the target hillside geological information base, and marking the initial ground water level height asp "represents the number of the p" th groundwater level monitoring point, p "=1", 2 ",..q";
the water level sensor detects the water level height of each water level monitoring point in each target hillside in real time, marks the water level height as a hip ", and substitutes the water level height of each water level monitoring point in each target hillside into a formulaObtaining the ground water level variation epsilon of each target hillside i Wherein q' represents the number of ground water level monitoring points, and phi represents a ground water level variation correction factor of a preset target hillside.
5. The intelligent geological disaster monitoring cloud platform based on the digital twin technology as set forth in claim 1, wherein: the specific analysis process in the target hillside geological data analysis module is as follows:
slope appearance variable alpha of each target hillside i Deep horizontal displacement variable χ of soil body i And the ground water level variation epsilon i Substitution into formula z i ＝τ 1 *α i +τ 2 *χ i +τ 3 *ε i Obtaining disaster risk coefficient z of each target hillside i Wherein τ 1 、τ 2 、τ 3 Respectively representing the preset weight factors of the slope appearance deformation quantity, the soil deep horizontal displacement change quantity and the groundwater level change quantity of the target hillside;
comparing the disaster risk coefficient of each target mountain slope with a preset disaster risk coefficient threshold value of the mountain slope, if the disaster risk coefficient of a certain target mountain slope is larger than or equal to the preset disaster risk coefficient threshold value of the mountain slope, then the target mountain slope has disaster risk, and sending the serial number of the target mountain slope to a disaster monitoring cloud platform for early warning, otherwise, the target mountain slope temporarily has no disaster risk, and the geological data of the target mountain slope are stored in a target mountain slope geological information base.
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