CN114282717B - Multi-target intelligent optimization site selection method for earthquake emergency observation flow table - Google Patents
Multi-target intelligent optimization site selection method for earthquake emergency observation flow table Download PDFInfo
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Abstract
The invention discloses a multi-target intelligent optimization site selection method for an earthquake emergency observation flow table, and belongs to the field of site selection of earthquake emergency observation flow tables. The invention comprises the following steps: determining earthquake major seisms to be tracked and monitored, fracture zones where the earthquake major seisms are located and fracture directions, configuring a deployment space range of a flow station, obtaining an unfavorable site element set in the deployment range from a site selection basic database, solving an optimal pareto solution set by adopting a multi-objective optimization method, screening a candidate optimal scheme of the flow station from the optimal solution set, selecting a bedrock area in the range around each site, slightly moving the bedrock area to bedrock, avoiding unfavorable site elements with small areas, and obtaining a final site selection scheme. The invention can provide a visual optimized site selection decision process and realize an optimized site selection scheme for laying the earthquake emergency observation flow table in a certain range of the earthquake region, thereby producing a high-quality earthquake aftershock sequence list, improving the rationality and timeliness of site selection of the earthquake emergency observation flow table and the earthquake monitoring capability of the earthquake region.
Description
Technical Field
The invention belongs to the field of site selection of earthquake emergency observation flow stations, and particularly relates to a multi-target intelligent optimization site selection method of an earthquake emergency observation flow station.
Background
Earthquake frequently occurs in China, is one of the most serious countries of earthquake disasters in the world, and the establishment of the earthquake measuring emergency mobile station is important work for improving emergency capacity and reducing earthquake disaster loss. The flow monitoring platform network (flow platform network) is mainly used for pre-earthquake observation before major earthquake and afterearthquake monitoring after the earthquake. The method is used for carrying out high-precision seismic positioning as the encrypted observation of the earthquake before the earth earthquake, carrying out dynamic tracking monitoring on the regional seismic activity background which is likely to generate the major earthquake, and serving for regional seismic activity research and earthquake prediction research; the method is used for on-site aftershock monitoring after the major earthquake, records aftershock activity change after the major earthquake, provides basis for judging the development trend of the earthquake, and also accumulates basic data for further researching the seismic source characteristics and exploring the occurrence and development processes of the earthquake.
The problem of site selection of the earthquake emergency observation mobile station is to quickly, reasonably and effectively select the site for the emergency observation mobile station to encrypt the existing earthquake platform network and improve the monitoring capability and the positioning precision of the platform network after the earthquake occurs. The existing earthquake detection emergency mobile station needs a certain time for establishing a flow, the timeliness is low, and whether the monitoring efficiency is maximized or not needs to be further judged. By researching the intelligent site selection method of the earthquake-measuring emergency mobile station and utilizing the evolutionary optimization algorithm, the emergency observation mobile station is output by software at the first time after an earthquake to establish a reference site, so that the earthquake monitoring capability and the emergency capability can be improved to a certain extent.
At present, the most common earthquake measuring emergency mobile station establishing method mainly adopts manual measurement and calculation, and adopts a heuristic method to determine the position of a station on a map according to experience. After an earthquake occurs, personnel and equipment need to be dispatched by a local earthquake bureau, and a place suitable for arranging an emergency observation flow table is found by experience through continuous manual survey within a certain range of an area where the earthquake occurs. The site selection scheme has long operation time, is difficult to meet the requirement of high timeliness of earthquake emergency monitoring, and meanwhile, whether a mobile monitoring network arranged according to experience is reasonable or not and whether the monitoring efficiency is maximized or not cannot be judged. In the implementation process of the technical scheme of the invention, the inventor finds that: if the emergency mobile platform layout scheme can be obtained at the first time after the earthquake occurs by utilizing the artificial intelligence optimization algorithm, not only is a great part of artificial cost saved, but also the construction of the emergency observation mobile platform network can be more scientifically and effectively established.
Disclosure of Invention
The invention aims to: the method for multi-target intelligent optimization site selection of the earthquake emergency observation mobile station is provided, and the defects that the existing method for site selection of the emergency observation mobile station is low in timeliness, high in labor cost and incapable of judging reasonability and accuracy are overcome.
The invention provides a multi-target intelligent optimization site selection method for an earthquake emergency observation mobile station, which comprises the following steps:
step 1, determining earthquake major earthquake and aftershock information to be tracked and monitored;
step 3, determining the surface fracture length L;
step 5, acquiring an unfavorable site element set P in a deployment range S from an addressing basic database;
step 6, converting the unfavorable site elements (surface elements) with the area larger than or equal to the first threshold into site selection constraint conditions C, namely converting the unfavorable site elements with the larger area into the site selection constraint conditions C according to the site selection rule of the mobile station;
step 7, solving an optimal Pareto (Pareto) solution set X of the multi-target mobile station site selection problem with the constraint condition C in a deployment range S by adopting a multi-target optimization method, wherein each solution of the solution set X represents the distribution condition of stations of all mobile stations in a mobile station network (platform network for short) instead of a single mobile station deployment position; that is, each solution of the solution set X represents the distribution position of each flow table of a group of flow tables under the requirement of the specified number of deployed flow tables, and the number of flow tables in each group corresponds to the number of deployed flow tables.
Step 8, selecting a recommended candidate solution set from the solution set X based on a specified screening rule, thereby obtaining a candidate optimal scheme of the mobile station;
step 9, selecting a bedrock area in the appointed neighborhood of each site of the candidate optimal scheme of the mobile platform to obtain a surrounding bedrock area of each site, and moving the site to the surrounding bedrock area to obtain an updated candidate optimal scheme of the mobile platform;
step 10, detecting each site of the updated mobile station candidate optimal scheme, and if the site is located in an avoidance area in the unfavorable site element set P, moving the current site out of the avoidance area according to a specified moving rule to obtain a seismic monitoring mobile station site selection scheme; the avoidance area refers to the unfavorable field elements with the area smaller than a first threshold value or the unfavorable field elements with the area of zero in the unfavorable field element set P.
Further, the site selection method further comprises a step 11 of taking the field solution of the recommended candidate solution set as a supplement scheme when the number of the currently obtained site selection schemes of the earthquake monitoring mobile station does not meet the requirement, namely a user needs more site selection schemes for comparison decision, moving each site of the supplement scheme to a surrounding bedrock area, monitoring whether each site of the supplement scheme is located in an avoidance area in the unfavorable site element set P, and if so, moving the current site out of the avoidance area according to a specified moving rule to obtain the site selection supplement scheme of the earthquake monitoring mobile station.
Preferably, the fracture direction is determined based on the distribution of aftershocks.
Preferably, the surface fracture length L is determined by:
obtaining a mapping between the seismic surface fracture length and the magnitude based on historical data regression statistics: m = α logL + b, where M represents the magnitude of the earthquake (principal), and the parameters α and b represent two regression coefficients;
based on the current magnitude M, the surface fracture length L is calculated according to the formula M = α logL + b.
Preferably, in step 7, the optimization objectives (expected objectives) of the multi-objective rover addressing problem are to minimize the (station) gap angle of the station network and maximize the seismic positioning accuracy, so as to be directed to the positioning accuracy objectives and the distribution uniformity objectives in the seismic emergency rover addressing.
Preferably, the set of recommended candidate solutions includes two extreme solutions x 1 And x 2 And a inflection point solution x 0 Wherein the extreme solution x 1 And x 2 Respectively obtaining an optimal solution and an inflection point solution x on two targets of minimizing a clearance angle and minimizing positioning precision 0 To achieve a solution for a compromise situation on both objectives.
Preferably, the movement rule of the avoidance region is as follows: minimum mobile cost rule.
Preferably, the multi-objective optimization method includes, but is not limited to: particle swarm algorithm and genetic algorithm.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that: the invention provides an efficient intelligent site selection method and system for an earthquake emergency flow earthquake measurement table. In the invention, the multi-target intelligent optimization site selection method for the earthquake flow monitoring station can be combined with a GIS (geographic information system), can provide a visual optimization site selection decision process, and realizes an optimization site selection scheme for arranging earthquake detection emergency flow stations in a certain range of an earthquake region, thereby producing a high-quality earthquake aftershock sequence directory, greatly improving the rationality and timeliness of site selection of the earthquake emergency flow stations, and improving the earthquake monitoring capability of the earthquake region.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic processing procedure diagram of a multi-objective intelligent optimization addressing method for a seismic emergency observation flow station according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the result of the solution of the site selection optimization case in the embodiment of the present invention, where (2-a), (2-b), and (2-c) respectively represent three candidate optimal solutions for the earthquake emergency observation mobile station.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The existing earthquake observation mobile station site selection method mainly adopts manual measurement and calculation, and adopts a heuristic method to determine the deployment position of a station on a map according to experience, so that the aftershock positioning precision and the clearance angle are satisfied as much as possible. The method for selecting the site of the mobile station has large search space and more constraint conditions, so that an optimal site selection scheme among a plurality of optimization targets such as positioning accuracy, clearance angle and the like is difficult to obtain. Meanwhile, the site selection scheme has long operation time and is difficult to meet the requirement of high timeliness of earthquake emergency monitoring. In order to improve the rationality and timeliness of site selection of an earthquake emergency observation flow station and improve earthquake monitoring capability of an earthquake region, the embodiment of the invention provides a multi-target intelligent optimization site selection method of the earthquake emergency observation flow station, and the method is shown in figure 1 and specifically comprises the following steps:
step 1, determining a seismic dominant earthquake (e) to be tracked and monitored;
step 3, determining the surface fracture length (L);
step 5, acquiring an unfavorable site element set (P) in the deployment range S from the site selection basic database;
step 6, adverse site elements with large area (the area in a geographic information system management GIS is more than or equal to A (the preferred value is 100 km) are treated according to the emergency observation mobile station site selection rule 2 ) The surface element) is converted into an addressing constraint condition (C);
the unfavorable site elements are various in types and large in quantity, and are projected on a map to be irregular discontinuous areas such as towns, rivers, lakes, mountains, reservoirs, quarries, reciprocating operation factories and railways of automobiles and trains, and the like frequently appear, so that the unfavorable site elements are converted into site selection constraint conditions which belong to large-scale constraint conditions difficult to process. Only unfavorable site factors with large area (such as large-area lakes, reservoirs and the like) are converted into site selection constraint conditions, so that the convergence speed of the optimization algorithm and the number of feasible solutions can be improved.
Step 7, solving an optimal Pareto solution set (X) of a multi-target (monitoring precision minimization and clearance angle minimization) mobile station site selection problem with a constraint condition C in a deployment range S by adopting a multi-target optimization method; namely, longitude and latitude coordinates of the mobile station are used as decision variables, and a multi-target constraint addressing optimization model of the seismic mobile station is constructed.
The multi-objective optimization algorithm usually outputs Pareto optimal solution sets in an external archive to a decision maker, but the decision maker is often confused by the numerous optimal solution sets. The decision maker usually wants to examine what optimization degree other targets can achieve when a certain target reaches the best, or wants to reach the best compromise situation at each target, then compares the nearby Pareto optimal solutions, and then makes the final scheme selection. The coarse-then-fine decision behavior mode firstly provides the optimal solution with special significance, namely the interesting solution, for the decision maker, and then provides a plurality of neighbor solutions around the selected interesting solution for the decision maker.
Step 8, selecting two extreme solutions (X) in the optimal solution set X 1 And x 2 ) And a solution of inflection point (x) 0 ) Is a recommended candidate solution;
step 9, selecting a bedrock area in the surrounding range (beta km) of each site of the candidate optimal scheme of the earthquake emergency observation mobile station, and micro-moving the site to the bedrock;
step 10, according to the mobile station site selection rule, carrying out small-range avoidance (avoidance is carried out in a mode of minimum movement cost) on the basis of the unfavorable site element with a small area (the surface element with the area smaller than A in the GIS) or an area of 0 (the point or line element in the GIS), and obtaining a recommended candidate solution (x) after avoiding the unfavorable site 1 ′，x 2 ′，x 0 ') as an earthquake monitoring mobile station addressing scheme for user decision-making;
unfavorable site elements with small areas (surface elements with areas smaller than A in the GIS) or areas of 0 (point or line elements in the GIS) are not converted into site selection constraint conditions in site selection optimization solution, and the mobile station reference sites obtained by an optimization algorithm are possibly located in the unfavorable sites, so that whether the obtained candidate solutions are located in the unfavorable sites needs to be judged, and the mobile stations which do not meet the requirements are moved out of the unfavorable sites according to the minimum movement cost rule.
Further, when the user needs more addressing schemes for comparison decision, the user can select the addressing scheme from the available addressing schemesSelecting X in Pareto optimal solution set X 1 ，x 2 And x 0 And adding the neighborhood solution to the earthquake monitoring mobile station site selection scheme after avoidance calculation.
As a possible implementation manner, the collection, processing and arrangement of the address selection basic data of the address selection basic database can be implemented by the following manners:
(1) and collecting the basic data of the site selection of the mobile station. The method is characterized in that a basic database built from projects such as an earthquake emergency command technical system and national social service engineering and other approaches are used for collecting data such as the existing earthquake monitoring capability, the earthquake table network layout, the bedrock characteristics, the background noise, the activity fracture, the distribution of interference sources (railway roads, thermal power stations, oil pipelines and the like), historical earthquakes, electric power traffic, communication safety and the like in an application demonstration monitoring area.
(2) And processing and finishing the basic data. And performing derivative calculation processing on the basic data according to the requirements of the seismic addressing model to obtain intermediate variable data required by the optimization model, and making element layers and registering on the spatial data.
(3) Modeling a database and warehousing the data. And designing and creating a database structure mode, and loading and storing the processed and manufactured data.
The embodiment of the invention is further described by taking the collected basic data of historical earthquake information, activity fracture, existing table net layout, cities, towns, lakes, roads and the like in Hunan province as an example.
And performing derivative calculation processing on the collected basic data to obtain intermediate variable data required by the optimization model.
And then acquiring the magnitude, the coordinate and corresponding aftershock information of the earthquake major earthquake to be tracked, tracked and monitored from the site selection basic database, calculating the fracture length (L) of the earthquake earth surface of the fault according to the relational expression of the magnitude of the major earthquake and the fracture length of the earthquake earth surface, and determining the fracture direction according to the aftershock spatial distribution, so that the deployment space range of the mobile station can be determined to be the areas on two sides of the fault with the length L along the fracture direction.
Acquiring earthquake monitoring fixed station set and unfavorable site in deployment range from site selection basic databaseCollecting elements, and collecting area greater than or equal to 100km in GIS 2 The surface element of (1) is converted into an addressing constraint condition.
Based on the characteristics of the mobile station site selection problem, a multi-target particle swarm optimization algorithm with constraints is adopted to solve a multi-target constraint site selection optimization model to obtain X solution sets, each solution set is characterized according to a preset data structure, namely the solution set is characterized as a solution vector, the dimensionality of the solution vector is determined by the pre-deployment number of the mobile stations given by a user, each element of the solution vector identifies the position deployment of one mobile station, namely one solution represents the distribution of a station network, and the solution is the coordinates of a group of stations instead of the coordinates of one station.
And finally, avoiding adverse site elements with smaller area in a small range according to a mobile station site selection rule, avoiding adverse site factors which are not converted into site selection constraint conditions by the obtained candidate solution in a minimum movement cost mode according to the mobile station site selection rule, and obtaining a recommended candidate solution after avoiding as a seismic monitoring mobile station site selection scheme for a user to make decisions, wherein (2-a), (2-b) and (2-c) respectively represent three seismic emergency observation mobile station candidate optimal schemes, namely three deployment schemes corresponding to two extreme solutions and an inflection point solution in an optimal solution set X, as shown in FIG. 2.
The multi-target intelligent optimization site selection method for the earthquake emergency observation flow table provided by the embodiment of the invention can be combined with a GIS (geographic information system), can provide a visual optimization site selection decision process, and realizes an optimization site selection scheme for arranging the earthquake emergency observation flow table in a certain range of an earthquake region, so that a high-quality earthquake aftershock sequence directory is produced, the rationality and the timeliness of site selection of the earthquake emergency observation flow table are greatly improved, and the earthquake monitoring capability of the earthquake region is improved.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
What has been described above are merely some embodiments of the present invention. It will be apparent to those skilled in the art that various changes and modifications can be made without departing from the inventive concept thereof, and these changes and modifications can be made without departing from the spirit and scope of the invention.
Claims (4)
1. The multi-objective intelligent optimization site selection method for the earthquake emergency observation flow station is characterized by comprising the following steps:
step 1, determining earthquake major earthquake and aftershock information to be tracked and monitored;
step 2, determining a fracture zone where the main earthquake is located and the fracture direction of the fracture zone, wherein the fracture direction is determined based on the distribution of aftershocks;
step 3, determining the surface fracture length L:
the determination of the surface fracture length L is:
obtaining a mapping between the seismic surface fracture length and the magnitude based on historical data regression statistics: m = α logL + b, where M represents the magnitude of the earthquake and the parameters α and b represent two regression coefficients;
calculating the surface fracture length L according to the formula M = α logL + b based on the current magnitude M;
step 4, configuring a deployment space range S of the mobile station according to the space range condition that the mobile station is positioned in the area with the length L at the two sides of the fault along the fracture direction;
step 5, acquiring an unfavorable site element set P in a deployment range S from a preset site selection basic database;
step 6, converting the unfavorable site elements with the area larger than or equal to the first threshold value into site selection constraint conditions C, namely converting the unfavorable site elements with the larger area into the site selection constraint conditions C according to the site selection rule of the mobile station;
step 7, solving an optimal pareto solution set X of the multi-target mobile station site selection problem with the constraint condition C in a deployment range S by adopting a multi-target optimization method, wherein each solution of the solution set X represents the distribution position of each mobile station of a group of mobile stations, and the number of each group of mobile stations depends on the number of the deployment stations of the specified mobile stations; the optimization target of the multi-target mobile station site selection problem is to minimize the gap angle of the station network and maximize the seismic positioning precision;
step 8, selecting a recommended candidate solution set from the solution set X based on a specified screening rule to obtain a candidate optimal scheme of the mobile station;
the recommended candidate solution set comprises two extreme solutions x 1 And x 2 And a inflection point solution x 0 Wherein the extreme solution x 1 And x 2 Respectively obtaining an optimal solution and an inflection point solution x on two targets of minimizing a clearance angle and minimizing positioning precision 0 A solution to achieve a compromise situation on both objectives;
step 9, selecting a bedrock area in the appointed neighborhood of each site of the candidate optimal scheme of the mobile platform to obtain a surrounding bedrock area of each site, and moving the site to the surrounding bedrock area to obtain an updated candidate optimal scheme of the mobile platform;
step 10, detecting each site of the updated mobile station candidate optimal scheme, and if the site is located in an avoidance area in the unfavorable site element set P, moving the current site out of the avoidance area according to a specified moving rule to obtain a seismic monitoring mobile station site selection scheme; the avoidance area refers to an unfavorable site element with an area smaller than a first threshold value or an unfavorable site element with an area of zero in the unfavorable site element set P;
the moving rule of the avoidance area is as follows: minimum mobile cost rule.
2. The method as claimed in claim 1, further comprising a step 11 of taking a domain solution of the recommended candidate solution set as a complementary solution if the number of the currently obtained seismic-monitoring mobile-station site selection solutions does not meet the requirement, that is, if the user needs more site selection solutions for the comparison decision, moving each site of the complementary solution to a surrounding bedrock area thereof, then monitoring whether each site of the complementary solution is located in an avoidance area in the adverse site element set P, and if so, moving the current site out of the avoidance area according to an assigned moving rule to obtain the seismic-monitoring mobile-station site selection complementary solution.
3. The method of claim 1, wherein the multi-objective optimization method is a particle swarm algorithm or a genetic algorithm.
4. The method of claim 1, wherein the first threshold is 100km 2 。
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