CN117894157A - Earthquake disaster early warning system based on big data - Google Patents
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Abstract
The invention discloses a seismic disaster early warning system based on big data, and particularly relates to the technical field of disaster early warning, which comprises a soil layer monitoring module, a soil layer data collecting module, a soil layer data analyzing module, a disaster graph drawing module, a checking module and a seismic disaster early warning module; the soil layer monitoring module is used for monitoring data of soil layers with different depths; the soil layer data collection module is used for summarizing the data monitored by the soil layers with different depths in the soil layer monitoring module to obtain a soil layer data set and transmitting the soil layer data set to the soil layer data analysis; according to the method, the dynamic change of the soil layer can be monitored and analyzed in real time, the area and the intensity of the earthquake possibly occurring are predicted, the earthquake disaster early warning is realized, the disaster graph can be drawn according to the preset disaster graph drawing strategy and the soil layer analysis set, the prediction result is checked and corrected, the earthquake disaster early warning efficiency and accuracy are effectively improved, the early warning is realized, and the loss caused by the earthquake disaster is reduced.
Description
Technical Field
The invention relates to the technical field of disaster early warning, in particular to a seismic disaster early warning system based on big data.
Background
The earthquake has the characteristics of burst nature, destructive nature, unpredictable nature and high frequency, is used as a natural disaster, is easy to cause collapse of various buildings, damage to facility equipment, traffic communication interruption and other life line engineering damages, further causes loss of life and property of human beings, damage or service performance reduction of the structure can be caused due to the effect of earthquake, fire disaster, hurricane or various long-term loads in civil structure engineering, serious potential safety hazards are buried, further causes damage of personnel and property, the earthquake monitoring technology is continuously improved along with the development of technology, but earthquake prediction is still a worldwide problem, and the occurrence of earthquake has burst nature and unpredictability, often cannot determine earthquake influence areas at the first time, distributes early warning information to the public, and has poor early warning effect.
Disclosure of Invention
In order to achieve the above purpose, the present invention provides the following technical solutions:
The earthquake disaster early warning system based on big data comprises a soil layer monitoring module, a soil layer data collecting module, a soil layer data analyzing module, a disaster graph drawing module, a checking module and an earthquake disaster early warning module;
the soil layer monitoring module is used for monitoring data of soil layers with different depths;
The soil layer data collection module is used for summarizing the data monitored by the soil layers with different depths in the soil layer monitoring module to obtain a soil layer data set and transmitting the soil layer data set to the soil layer data analysis;
the soil layer data analysis module is used for respectively analyzing soil layers with different depths according to the soil layer data set, and then summarizing all analysis results into a soil layer analysis set and transmitting the soil layer analysis set to the disaster graph drawing module;
The disaster graph drawing module is used for drawing a disaster graph according to a preset disaster graph drawing strategy and a soil layer analysis set to obtain a first disaster graph;
the verification module is used for performing verification operation on the first disaster pattern, drawing the second disaster pattern according to a verification result and transmitting the second disaster pattern to the earthquake disaster early warning module;
the earthquake disaster early warning module is used for carrying out earthquake disaster early warning on the coverage area according to the disaster graph II.
In a preferred embodiment, the big data based earthquake disaster warning system further comprises a display module, wherein the display module is used for displaying all operation records of the big data based earthquake disaster warning system.
In a preferred embodiment, the soil layer monitoring module comprises m soil layer monitoring units, each soil layer monitoring unit comprises a monitoring cable and a mechanical sensor, the monitoring cable is used for sensing the soil layer shearing force change, the mechanical sensor is connected with the monitoring cable, and the mechanical sensor is used for detecting the tension dynamic change of the monitoring cable.
In a preferred embodiment, the soil layer data collection module is configured to collect data monitored by soil layers with different depths in the soil layer monitoring module, and the obtaining of the soil layer data set refers to:
The soil layer data collection module collects depth data of a monitoring soil layer of a single soil layer monitoring unit, longitude and latitude coordinate data of a monitoring site, tensile force data of a monitoring cable detected by a mechanical sensor in real time, reinforcement angle data of the monitoring cable, internal friction angle data of the monitoring soil layer, viscosity and aggregation force in the monitoring soil layer and inclination angle data of the monitoring soil layer, the depth data of the monitoring soil layer of the single soil layer monitoring unit is marked as Hi, tensile force data of the monitoring cable detected by the mechanical sensor in real time is marked as Li, longitude and latitude coordinate data (Ji and Wi) of the monitoring site, reinforcement angle data of the monitoring cable is marked as Gi, the internal friction angle data of the monitoring soil layer is marked as Mi, the viscosity and aggregation force in the monitoring soil layer is marked as Ci, and the inclination angle data of the monitoring soil layer is marked as Xi, and then the monitoring data of all the soil layer monitoring units are summarized to obtain a soil layer data set.
In a preferred embodiment, the soil layer data analysis module is configured to perform analysis operations on soil layers with different depths according to the soil layer data set, and then summarizing all analysis results into the soil layer analysis set refers to:
Step A1, calculating to obtain a soil layer data analysis value through a formula, wherein FXI is the soil layer data analysis value;
A2, comparing the soil layer data analysis value FXI with a preset early warning threshold value, and marking the soil layer data analysis value FXI as dangerous data if the soil layer data analysis value FXI is larger than or equal to the preset early warning threshold value; if the soil layer data analysis value Fxi is smaller than a preset early warning threshold value, marking the soil layer data analysis value Fxi as safety data;
step A3, acquiring depth data Hi of the monitored soil layer corresponding to the dangerous data, and then analyzing and processing the depth data Hi of the monitored soil layer and a preset standard depth value HB to obtain a first sweep radius Ri, wherein Ri=fi, hi/HB and fi are specific proportionality coefficients corresponding to the depth data Hi of the monitored soil layer;
And A4, summarizing the first sweep radius Ri obtained in the step A3 and longitude and latitude coordinate data (Ji, wi) of the monitoring site corresponding to the dangerous data to obtain a soil layer analysis subset, and summarizing all the soil layer analysis subsets to obtain a soil layer analysis set.
The disaster graph drawing module is used for drawing a disaster graph according to a preset disaster graph drawing strategy and a soil layer analysis set, and the first step of obtaining the disaster graph is as follows:
searching longitude and latitude coordinate data (Ji, wi) of a monitoring site on a map according to the soil layer analysis subset, setting the longitude and latitude coordinate data as a circle center, and then drawing circles by taking a first sweep radius Ri as a radius of a drawn circle to obtain a plurality of circles;
And secondly, determining a central point position among a plurality of circles, setting longitude and latitude coordinate data (Ji, wi) of the monitoring points in the soil layer analysis subset obtained in the first step as the central point position, and drawing circles by taking the central point position as the circle center, so that the drawn circles cover all the circles in the first step and are inscribed with one of the circles only to obtain a disaster graph I.
In a preferred embodiment, the verification module is configured to perform a verification operation on the disaster graph one, and draw, according to a verification result, that:
Overlapping a preset seismic band diagram and a disaster graph I, numbering seismic bands in the seismic band diagram and marking the numbered seismic bands as BHi;
judging whether a superposition area exists after the preset seismic zone diagram and the disaster graph I are superposed, if so, recording the superposition area Si and the corresponding seismic zone number BHi, and then obtaining a first influence value through calculation: the method comprises the steps that when the Y1 = and the area Si of a superposition area are superposed, a first influence coefficient preset by a corresponding seismic zone number BHi is/> ,, the area size of a disaster pattern I, Y1 is a first influence value, and if no superposition area exists, the numerical value of the first influence value Y1 is determined to be 1;
Step three, judging whether a preset seismic band diagram and a disaster diagram I are overlapped, if so, recording a nearest distance value Zi and a corresponding seismic band number BHi, and then obtaining a second influence value through calculation: y2= , a first influence coefficient preset by the seismic band number BHi corresponding to the nearest distance value Zi is/> ,/>, a preset safe distance threshold value, and Y2 is a second influence value; if not, determining the value of the second influence value Y2 as 1;
Step four, calculating a third influence value through a formula: y3=a1×y1+a2×y2, wherein a 1and a2 are positive numbers and the sum of the positive numbers and the negative number is 1, and then determining whether Y3 is equal to 1 in value, if yes, the disaster pattern two is the disaster pattern one; if not, determining a drawing radius r2 of the disaster graph II, wherein r2 = Y3 r1, r1 is the drawing radius of the disaster graph I, taking the circle center of the disaster graph I as the circle center of the disaster graph II, and drawing a circle by using the drawing radius r2, wherein the obtained graph is the disaster graph II.
The invention has the technical effects and advantages that:
According to the invention, through the big data and the earthquake monitoring module, dynamic changes of soil layers, namely shear force changes of soil layers with different depths, can be monitored and analyzed in real time, the accuracy and timeliness of earthquake early warning are improved, the area and the intensity of the earthquake possibly occurring are predicted, so that the early warning of the earthquake disaster is realized, meanwhile, the disaster graph can be drawn according to the preset disaster graph drawing strategy and the soil layer analysis set, the influence range of the earthquake disaster can be more intuitively understood, and the system is also provided with the verification module, the prediction result can be verified and corrected, the early warning accuracy is improved, the early warning efficiency and the early warning accuracy can be effectively improved, the early warning is realized, and the loss caused by the earthquake disaster is reduced.
According to the invention, the soil layer is analyzed by collecting the data of the soil layer monitoring module, and whether the earthquake danger exists or not is judged, so that the early warning of the earthquake disaster is realized, the disaster graph drawing module is arranged to draw the disaster graph I and the disaster graph II according to the analysis result of the soil layer, intuitively display the earthquake influence range, help relevant departments to formulate countermeasures, and the arranged verification module is used for verifying the disaster graph I, so that the early warning accuracy is further improved.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
Fig. 1 is a schematic diagram of an earthquake disaster warning system based on big data in 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.
Example 1
The earthquake disaster early warning system based on big data comprises a soil layer monitoring module, a soil layer data collecting module, a soil layer data analyzing module, a disaster graph drawing module, a checking module and an earthquake disaster early warning module; the system comprises a soil layer monitoring module, a soil layer data collecting module, a soil layer data analyzing module, a disaster graph drawing module, a checking module and a seismic disaster early warning module, which are connected in a communication manner; the soil layer monitoring module is used for monitoring data of soil layers with different depths; the soil layer data collection module is used for summarizing the data monitored by the soil layers with different depths in the soil layer monitoring module to obtain a soil layer data set and transmitting the soil layer data set to the soil layer data analysis; the soil layer data analysis module is used for respectively analyzing soil layers with different depths according to the soil layer data set, and then summarizing all analysis results into a soil layer analysis set and transmitting the soil layer analysis set to the disaster graph drawing module; the disaster graph drawing module is used for drawing a disaster graph according to a preset disaster graph drawing strategy and a soil layer analysis set to obtain a first disaster graph; the verification module is used for performing verification operation on the first disaster pattern, drawing the second disaster pattern according to a verification result and transmitting the second disaster pattern to the earthquake disaster early warning module; the earthquake disaster early warning module is used for carrying out earthquake disaster early warning on the coverage area according to the disaster graph II.
The soil layer monitoring module comprises m soil layer monitoring units, the positions and depths of the m soil layer monitoring units are different, each soil layer monitoring unit comprises a monitoring cable and a mechanical sensor, the monitoring cables are used for sensing the change of soil layer shearing force, the mechanical sensors are connected to the monitoring cables, the mechanical sensors are used for detecting the dynamic change of tensile force of the monitoring cables, and m is a positive integer.
The soil layer data collection module is used for summarizing the data monitored by soil layers with different depths in the soil layer monitoring module, and the obtained soil layer data set is:
The soil layer data collection module collects depth data of a monitoring soil layer of a single soil layer monitoring unit, longitude and latitude coordinate data of a monitoring site, tension data of a monitoring cable detected in real time by a mechanical sensor, reinforcement angle data of the monitoring cable, internal friction angle data of the monitoring soil layer, cohesive force in the monitoring soil layer and inclination angle data of the monitoring soil layer, the depth data of the monitoring soil layer of the single soil layer monitoring unit is marked as Hi, tension data of the monitoring cable detected in real time by the mechanical sensor is marked as Li, in order to enable the monitoring cable to accurately sense shearing force among soil layers, prestress is applied to the monitoring cable when the monitoring cable is set, an initial value of the prestress can be measured by the mechanical sensor, longitude and latitude coordinate data (Ji and Wi) of the monitoring site, reinforcement angle data of the monitoring cable is marked as Gi, internal friction angle data of the monitoring soil layer is marked as Mi, cohesive force in the monitoring soil layer is marked as Ci, the monitoring soil layer inclination angle data of the monitoring soil layer is marked as Xi, and monitoring soil layer inclination angle data of all the monitoring soil layer units are summarized to obtain a soil layer data set, i is a project number, i=1, 2, 3,4, … … and p is an integer and is not repeated.
The soil layer data analysis module is used for respectively analyzing soil layers with different depths according to the soil layer data set, and then summarizing all analysis results into a soil layer analysis set, which means that:
Step A1, calculating to obtain a soil layer data analysis value through a formula, wherein FXI is the soil layer data analysis value; for the soil layer, the self gravity of the upper soil layer is a constant function of the volume and the volume weight, under the condition that the properties of the water content and the like of the soil layer are unchanged, the self gravity of the upper soil layer is constant, along with the formation of a damaged surface and the generation of dislocation displacement, the friction resistance of the upper soil layer on a section caused by internal friction force and cohesive force is gradually reduced, the internal stress is gradually increased, the change of the internal stress can reflect the change of the undetectable shearing strength and shearing force between the upper soil layer and the lower soil layer, thereby reflecting the change of the stability between the soil layers, and finally, along with the continuous increase of the shearing force, namely the soil layer data analysis value FXI, the dislocation movement between the upper soil layer and the lower soil layer is started, and the earthquake danger exists;
a2, comparing the soil layer data analysis value FXI with a preset early warning threshold value, and marking the soil layer data analysis value FXI as dangerous data if the soil layer data analysis value FXI is larger than or equal to the preset early warning threshold value, wherein the upper soil layer and the lower soil layer start to move in a staggered mode at the moment, and earthquake danger exists; if the soil layer data analysis value Fxi is smaller than a preset early warning threshold value, marking the soil layer data analysis value FXI as safety data, wherein the upper soil layer and the lower soil layer cannot move in a staggered manner at the moment, and earthquake danger does not exist;
Step A3, acquiring depth data Hi of the monitored soil layer corresponding to the dangerous data, and then analyzing and processing the depth data Hi of the monitored soil layer and a preset standard depth value HB to obtain a first sweep radius Ri, wherein Ri=fi, hi/HB and fi are specific proportionality coefficients corresponding to the depth data Hi of the monitored soil layer; the first sweep radius Ri obtained through calculation shows that the situation that the depth data Hi is a seismic source is compared with the situation that the preset standard depth value HB is the seismic source, the coverage radius influenced by the situation is the first sweep radius Ri, and as the number of detection points in the invention is far more than one, the next integration processing is needed, and a disaster graph drawing module is used for drawing a disaster graph according to a preset disaster graph drawing strategy and a soil layer analysis set, so that the implementation step of the disaster graph I is specifically explained;
And A4, summarizing the first sweep radius Ri obtained in the step A3 and longitude and latitude coordinate data (Ji, wi) of the monitoring site corresponding to the dangerous data to obtain a soil layer analysis subset, and summarizing all the soil layer analysis subsets to obtain a soil layer analysis set.
The disaster graph drawing module is used for drawing a disaster graph according to a preset disaster graph drawing strategy and a soil layer analysis set, and the first step of obtaining the disaster graph is as follows:
searching longitude and latitude coordinate data (Ji, wi) of a monitoring site on a map according to the soil layer analysis subset, setting the longitude and latitude coordinate data as a circle center, and then drawing circles by taking a first sweep radius Ri as a radius of a drawn circle to obtain a plurality of circles;
And secondly, determining a central point position among a plurality of circles, setting longitude and latitude coordinate data (Ji, wi) of a monitoring point in the soil layer analysis subset obtained in the first step as the central point position, and carrying out circle drawing by taking the central point position as the center of a circle, so that all circles in the first step are covered by the drawn circles and only inscribe with one circle in the circles, and a disaster graph I is obtained.
The verification module is used for performing verification operation on the first disaster graph and drawing two fingers of the disaster graph according to the verification result:
Overlapping a preset seismic band diagram and a disaster graph I, numbering seismic bands in the seismic band diagram and marking the numbered seismic bands as BHi; the seismic band map refers to a map of each divided seismic band coverage area, and because of the movement of the crust, the seismic band map is required to be updated, the preset seismic band map is provided by related experts and related departments, and the seismic band map is the prior art and is not repeated in detail; overlapping the preset seismic band diagram and the disaster graph I means that the disaster graph I is placed on the preset seismic band diagram according to the actual position, and a coverage area of the disaster graph I can be circled on the preset seismic band diagram;
Judging whether a superposition area exists after the preset seismic zone diagram and the disaster graph I are superposed, if so, recording the superposition area Si and the corresponding seismic zone number BHi, and then obtaining a first influence value through calculation: the method comprises the steps that when the Y1 = and the area Si of a superposition area are superposed, a first influence coefficient preset by a corresponding seismic zone number BHi is/> ,, the area size of a disaster pattern I, Y1 is a first influence value, and if no superposition area exists, the numerical value of the first influence value Y1 is determined to be 1; the first influence coefficient/> represents the influence intensity of the seismic band number BHi on the earthquake occurring in the covered area;
Step three, judging whether a preset seismic band diagram and a disaster diagram I are overlapped, if so, recording a nearest distance value Zi and a corresponding seismic band number BHi, and then obtaining a second influence value through calculation: y2= , a first influence coefficient preset by the seismic band number BHi corresponding to the nearest distance value Zi is/> ,/>, a preset safe distance threshold value, and Y2 is a second influence value; if not, determining the value of the second influence value Y2 as 1; the second influence value Y2 represents the influence intensity of the seismic band number BHi on the occurrence of the earthquake in the area with the peripheral nearest distance smaller than the preset safe distance threshold value, and it should be noted that if the seismic band number BHi is already involved in the calculation in the second step, the secondary statistics will not be performed in the third step;
Step four, calculating a third influence value through a formula: y3=a1×y1+a2×y2, wherein a1 and a2 are positive numbers and the sum of the positive numbers and the negative number is 1, and then determining whether Y3 is equal to 1 in value, if yes, the disaster pattern two is the disaster pattern one; if not, determining a drawing radius r2 of the disaster graph II, wherein r2 = Y3 r1, r1 is the drawing radius of the disaster graph I, taking the circle center of the disaster graph I as the circle center of the disaster graph II, drawing a circle by using the drawing radius r2, and obtaining the graph which is the disaster graph II, wherein the disaster graph II is the finally determined area range influenced by the earthquake disaster, and the larger the coverage area of the disaster graph II is, the larger the earthquake intensity of the early warning is, the larger the disaster receiving area which is easy to cause is, and the more importance is needed to be put.
Example 2
The earthquake disaster early warning system based on big data comprises a soil layer monitoring module, a soil layer data collecting module, a soil layer data analyzing module, a disaster graph drawing module, a checking module and an earthquake disaster early warning module; the earthquake disaster early warning system based on the big data further comprises a display module, wherein the display module is used for displaying all operation records of the earthquake disaster early warning system based on the big data, and the soil layer monitoring module, the soil layer data collecting module, the soil layer data analyzing module, the disaster graph drawing module, the checking module, the earthquake disaster early warning module and the display module are in communication connection.
The soil layer monitoring module is used for monitoring data of soil layers with different depths; the soil layer monitoring module comprises m soil layer monitoring units, each soil layer monitoring unit comprises a monitoring cable and a mechanical sensor, the monitoring cable is used for sensing the change of soil layer shearing force, the mechanical sensor is connected to the monitoring cable, and the mechanical sensor is used for detecting the dynamic change of tensile force of the monitoring cable.
The soil layer data collection module is used for summarizing the data monitored by soil layers with different depths in the soil layer monitoring module, so as to obtain a soil layer data set and convey the soil layer data set to soil layer data analysis, and the soil layer data collection module specifically comprises: the soil layer data collection module collects depth data of a monitoring soil layer of a single soil layer monitoring unit, longitude and latitude coordinate data of a monitoring site, tensile force data of a monitoring cable detected by a mechanical sensor in real time, reinforcement angle data of the monitoring cable, internal friction angle data of the monitoring soil layer, viscosity and aggregation force in the monitoring soil layer and inclination angle data of the monitoring soil layer, the depth data of the monitoring soil layer of the single soil layer monitoring unit is marked as Hi, tensile force data of the monitoring cable detected by the mechanical sensor in real time is marked as Li, longitude and latitude coordinate data (Ji and Wi) of the monitoring site, reinforcement angle data of the monitoring cable is marked as Gi, the internal friction angle data of the monitoring soil layer is marked as Mi, the viscosity and aggregation force in the monitoring soil layer is marked as Ci, and the inclination angle data of the monitoring soil layer is marked as Xi, and then the monitoring data of all the soil layer monitoring units are summarized to obtain a soil layer data set.
The soil layer data analysis module is used for respectively analyzing soil layers with different depths according to the soil layer data set, and then summarizing all analysis results into a soil layer analysis set and conveying the soil layer analysis set to the disaster graph drawing module, and comprises the following steps: step A1, calculating to obtain a soil layer data analysis value through a formula, wherein FXI is the soil layer data analysis value;
A2, comparing the soil layer data analysis value FXI with a preset early warning threshold value, and marking the soil layer data analysis value FXI as dangerous data if the soil layer data analysis value FXI is larger than or equal to the preset early warning threshold value; if the soil layer data analysis value Fxi is smaller than a preset early warning threshold value, marking the soil layer data analysis value Fxi as safety data;
step A3, acquiring depth data Hi of the monitored soil layer corresponding to the dangerous data, and then analyzing and processing the depth data Hi of the monitored soil layer and a preset standard depth value HB to obtain a first sweep radius Ri, wherein Ri=fi, hi/HB and fi are specific proportionality coefficients corresponding to the depth data Hi of the monitored soil layer;
And A4, summarizing the first sweep radius Ri obtained in the step A3 and longitude and latitude coordinate data (Ji, wi) of the monitoring site corresponding to the dangerous data to obtain a soil layer analysis subset, and summarizing all the soil layer analysis subsets to obtain a soil layer analysis set.
The disaster graph drawing module is used for drawing a disaster graph according to a preset disaster graph drawing strategy and a soil layer analysis set to obtain a first disaster graph, and specifically comprises the following steps of: searching longitude and latitude coordinate data (Ji, wi) of a monitoring site on a map according to the soil layer analysis subset, setting the longitude and latitude coordinate data as a circle center, and then drawing circles by taking a first sweep radius Ri as a radius of a drawn circle to obtain a plurality of circles;
And secondly, determining a central point position among a plurality of circles, setting longitude and latitude coordinate data (Ji, wi) of the monitoring points in the soil layer analysis subset obtained in the first step as the central point position, and drawing circles by taking the central point position as the circle center, so that the drawn circles cover all the circles in the first step and are inscribed with one of the circles only to obtain a disaster graph I.
The verification module is used for performing verification operation on the first disaster graph, drawing the second disaster graph according to a verification result and conveying the second disaster graph to the earthquake disaster early warning module, and specifically comprises the following steps of: overlapping a preset seismic band diagram and a disaster graph I, numbering seismic bands in the seismic band diagram and marking the numbered seismic bands as BHi;
Judging whether a superposition area exists after the preset seismic zone diagram and the disaster graph I are superposed, if so, recording the superposition area Si and the corresponding seismic zone number BHi, and then obtaining a first influence value through calculation: the method comprises the steps that when the Y1 = and the area Si of a superposition area are superposed, a first influence coefficient preset by a corresponding seismic zone number BHi is/> ,, the area size of a disaster pattern I, Y1 is a first influence value, and if no superposition area exists, the numerical value of the first influence value Y1 is determined to be 1;
Step three, judging whether a preset seismic band diagram and a disaster diagram I are overlapped, if so, recording a nearest distance value Zi and a corresponding seismic band number BHi, and then obtaining a second influence value through calculation: y2= , a first influence coefficient preset by the seismic band number BHi corresponding to the nearest distance value Zi is/> ,/>, a preset safe distance threshold value, and Y2 is a second influence value; if not, determining the value of the second influence value Y2 as 1;
Step four, calculating a third influence value through a formula: y3=a1×y1+a2×y2, wherein a 1and a2 are positive numbers and the sum of the positive numbers and the negative number is 1, and then determining whether Y3 is equal to 1 in value, if yes, the disaster pattern two is the disaster pattern one; if not, determining a drawing radius r2 of the disaster graph II, wherein r2 = Y3 r1, r1 is the drawing radius of the disaster graph I, taking the circle center of the disaster graph I as the circle center of the disaster graph II, and drawing a circle by using the drawing radius r2, wherein the obtained graph is the disaster graph II.
The earthquake disaster early warning module is used for carrying out earthquake disaster early warning on the coverage area of the disaster graph II according to the disaster graph II, then the earthquake disaster early warning module receives the disaster graph II, and according to a preset early warning strategy, the coverage area size and the circle center position of the disaster graph II are divided into a plurality of earthquake influence areas with gradually attenuated earthquake intensity along the circle center, then earthquake disaster early warning signals are sent to responsible personnel or related departments of the earthquake influence areas, further, a plurality of subareas can be divided according to the radius along the disaster graph II, the early warning grade corresponding to the subareas which are closer to the circle center is higher, and then different early warning grade signals can be correspondingly sent to responsible personnel or related departments corresponding to the subareas to carry out different grade reminding.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
The foregoing is merely illustrative of the present application, and the present application 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 application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (7)
1. The earthquake disaster early warning system based on big data is characterized by comprising a soil layer monitoring module, a soil layer data collecting module, a soil layer data analyzing module, a disaster pattern drawing module, a checking module and an earthquake disaster early warning module;
the soil layer monitoring module is used for monitoring data of soil layers with different depths;
The soil layer data collection module is used for summarizing the data monitored by the soil layers with different depths in the soil layer monitoring module to obtain a soil layer data set and transmitting the soil layer data set to the soil layer data analysis;
the soil layer data analysis module is used for respectively analyzing soil layers with different depths according to the soil layer data set, and then summarizing all analysis results into a soil layer analysis set and transmitting the soil layer analysis set to the disaster graph drawing module;
The disaster graph drawing module is used for drawing a disaster graph according to a preset disaster graph drawing strategy and a soil layer analysis set to obtain a first disaster graph;
the verification module is used for performing verification operation on the first disaster pattern, drawing the second disaster pattern according to a verification result and transmitting the second disaster pattern to the earthquake disaster early warning module;
the earthquake disaster early warning module is used for carrying out earthquake disaster early warning on the coverage area according to the disaster graph II.
2. The big data based earthquake disaster warning system of claim 1, further comprising a display module for displaying all operational records of the big data based earthquake disaster warning system.
3. The earthquake disaster warning system based on big data of claim 2, wherein the soil layer monitoring module comprises m soil layer monitoring units, each soil layer monitoring unit comprises a monitoring cable and a mechanical sensor, the monitoring cable is used for sensing the soil layer shearing force change, the mechanical sensor is connected with the monitoring cable, and the mechanical sensor is used for detecting the tension dynamic change of the monitoring cable.
4. The earthquake disaster warning system based on big data as set forth in claim 3, wherein the soil layer data collection module is configured to collect data monitored by soil layers with different depths in the soil layer monitoring module, and the obtaining of the soil layer data set is:
The soil layer data collection module collects depth data of a monitoring soil layer of a single soil layer monitoring unit, longitude and latitude coordinate data of a monitoring site, tensile force data of a monitoring cable detected by a mechanical sensor in real time, reinforcement angle data of the monitoring cable, internal friction angle data of the monitoring soil layer, viscosity and aggregation force in the monitoring soil layer and inclination angle data of the monitoring soil layer, the depth data of the monitoring soil layer of the single soil layer monitoring unit is marked as Hi, tensile force data of the monitoring cable detected by the mechanical sensor in real time is marked as Li, longitude and latitude coordinate data (Ji and Wi) of the monitoring site, reinforcement angle data of the monitoring cable is marked as Gi, the internal friction angle data of the monitoring soil layer is marked as Mi, the viscosity and aggregation force in the monitoring soil layer is marked as Ci, and the inclination angle data of the monitoring soil layer is marked as Xi, and then the monitoring data of all the soil layer monitoring units are summarized to obtain a soil layer data set.
5. The earthquake disaster warning system based on big data as set forth in claim 4, wherein the soil layer data analysis module is configured to analyze soil layers with different depths according to the soil layer data set, and then summarizing all analysis results into a soil layer analysis set means that:
Step A1, calculating to obtain a soil layer data analysis value through a formula, wherein FXI is the soil layer data analysis value;
A2, comparing the soil layer data analysis value FXI with a preset early warning threshold value, and marking the soil layer data analysis value FXI as dangerous data if the soil layer data analysis value FXI is larger than or equal to the preset early warning threshold value; if the soil layer data analysis value Fxi is smaller than a preset early warning threshold value, marking the soil layer data analysis value Fxi as safety data;
step A3, acquiring depth data Hi of the monitored soil layer corresponding to the dangerous data, and then analyzing and processing the depth data Hi of the monitored soil layer and a preset standard depth value HB to obtain a first sweep radius Ri, wherein Ri=fi, hi/HB and fi are specific proportionality coefficients corresponding to the depth data Hi of the monitored soil layer;
And A4, summarizing the first sweep radius Ri obtained in the step A3 and longitude and latitude coordinate data (Ji, wi) of the monitoring site corresponding to the dangerous data to obtain a soil layer analysis subset, and summarizing all the soil layer analysis subsets to obtain a soil layer analysis set.
6. The earthquake disaster warning system based on big data as set forth in claim 5, wherein the disaster pattern drawing module is configured to draw a disaster pattern according to a preset disaster pattern drawing strategy and a soil layer analysis set, and the first step of obtaining the disaster pattern is:
searching longitude and latitude coordinate data (Ji, wi) of a monitoring site on a map according to the soil layer analysis subset, setting the longitude and latitude coordinate data as a circle center, and then drawing circles by taking a first sweep radius Ri as a radius of a drawn circle to obtain a plurality of circles;
And secondly, determining a central point position among a plurality of circles, setting longitude and latitude coordinate data (Ji, wi) of the monitoring points in the soil layer analysis subset obtained in the first step as the central point position, and drawing circles by taking the central point position as the circle center, so that the drawn circles cover all the circles in the first step and are inscribed with one of the circles only to obtain a disaster graph I.
7. The earthquake disaster warning system based on big data as set forth in claim 6, wherein the verification module is configured to perform a verification operation on the disaster graph one, and draw the disaster graph two fingers according to the verification result:
Overlapping a preset seismic band diagram and a disaster graph I, numbering seismic bands in the seismic band diagram and marking the numbered seismic bands as BHi;
Judging whether a superposition area exists after the preset seismic zone diagram and the disaster graph I are superposed, if so, recording the superposition area Si and the corresponding seismic zone number BHi, and then obtaining a first influence value through calculation: the method comprises the steps that when the Y1 = and the area Si of a superposition area are superposed, a first influence coefficient preset by a corresponding seismic zone number BHi is/> ,, the area size of a disaster pattern I, Y1 is a first influence value, and if no superposition area exists, the numerical value of the first influence value Y1 is determined to be 1;
Step three, judging whether a preset seismic band diagram and a disaster diagram I are overlapped, if so, recording a nearest distance value Zi and a corresponding seismic band number BHi, and then obtaining a second influence value through calculation: y2= , a first influence coefficient preset by the seismic band number BHi corresponding to the nearest distance value Zi is/> ,/>, a preset safe distance threshold value, and Y2 is a second influence value; if not, determining the value of the second influence value Y2 as 1;
Step four, calculating a third influence value through a formula: y3=a1×y1+a2×y2, wherein a 1and a2 are positive numbers and the sum of the positive numbers and the negative number is 1, and then determining whether Y3 is equal to 1 in value, if yes, the disaster pattern two is the disaster pattern one; if not, determining a drawing radius r2 of the disaster graph II, wherein r2 = Y3 r1, r1 is the drawing radius of the disaster graph I, taking the circle center of the disaster graph I as the circle center of the disaster graph II, and drawing a circle by using the drawing radius r2, wherein the obtained graph is the disaster graph II.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101887624A (en) * | 2010-07-21 | 2010-11-17 | 中国矿业大学(北京) | Method and system for advanced early warning and forecasting of earthquake disasters |
| JP2017184011A (en) * | 2016-03-30 | 2017-10-05 | リズム時計工業株式会社 | Emergency disaster information receiver |
| KR101872238B1 (en) * | 2018-01-25 | 2018-07-31 | 라성에너지(주) | Early warning system of earthquake |
| US20210134135A1 (en) * | 2019-10-30 | 2021-05-06 | University Of Science And Technology Beijing | Multi-system, multi-parameter, integrated, comprehensive early warning method and system for coal and rock dynamic disaster |
| KR102457909B1 (en) * | 2022-03-28 | 2022-10-25 | 주식회사 무한정보기술 | Monitoring system for early warning and integration management of disaster |
| CN116257541A (en) * | 2021-12-08 | 2023-06-13 | 中国电信国际有限公司 | Natural disaster emergency strategy execution method and device, electronic equipment and storage medium |
-
2024
- 2024-03-14 CN CN202410289881.7A patent/CN117894157A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101887624A (en) * | 2010-07-21 | 2010-11-17 | 中国矿业大学(北京) | Method and system for advanced early warning and forecasting of earthquake disasters |
| JP2017184011A (en) * | 2016-03-30 | 2017-10-05 | リズム時計工業株式会社 | Emergency disaster information receiver |
| KR101872238B1 (en) * | 2018-01-25 | 2018-07-31 | 라성에너지(주) | Early warning system of earthquake |
| US20210134135A1 (en) * | 2019-10-30 | 2021-05-06 | University Of Science And Technology Beijing | Multi-system, multi-parameter, integrated, comprehensive early warning method and system for coal and rock dynamic disaster |
| CN116257541A (en) * | 2021-12-08 | 2023-06-13 | 中国电信国际有限公司 | Natural disaster emergency strategy execution method and device, electronic equipment and storage medium |
| KR102457909B1 (en) * | 2022-03-28 | 2022-10-25 | 주식회사 무한정보기술 | Monitoring system for early warning and integration management of disaster |
Non-Patent Citations (6)
| Title |
|---|
| 任智源 等: "2016年全球地震海啸监测预警与数值模拟研究", 海洋科学, no. 06, 15 June 2017 (2017-06-15) * |
| 吴鹏 等: "基于地震烈度的预警系统设计与实现", 现代计算机, no. 08, 15 March 2013 (2013-03-15) * |
| 孙丽娜 等: "晋冀蒙交界地区地震危险性评估及地震活动性分析", 震灾防御技术, no. 01, 15 March 2020 (2020-03-15) * |
| 张浪平 等: "美国西部地区加卸载响应比的时空演化及地震活动性分析", 地震, no. 04, 15 October 2008 (2008-10-15) * |
| 高尚华;: "位移格林函数的精密计算及其在芦山Ms7.0地震中的应用", 大地测量与地球动力学, no. 02, 15 April 2015 (2015-04-15) * |
| 魏赛拉加 等: "青海地区环境的地震灾害信息预测模型研究", 华南地震, no. 04, 15 December 2019 (2019-12-15) * |
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