CN114897454B - Urban infrastructure group settlement evaluation method, electronic device and storage medium - Google Patents
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Abstract
The invention provides a city infrastructure group settlement evaluation method, electronic equipment and a storage medium, and belongs to the technical field of infrastructure settlement evaluation methods. The method comprises the following steps: s1, acquiring ground settlement parameters of urban infrastructure; s2, primarily dividing and secondarily dividing the infrastructure area; s3, dividing city infrastructure groups in a cross-region manner; and S4, obtaining the evaluation index of the ground settlement condition of the urban infrastructure group, and adding the ground settlement parameters of all infrastructures in the infrastructure group to obtain the ground settlement parameter value of a single infrastructure group. The method is based on urban ground settlement Insar monitoring data, the monitoring precision of the data on urban ground settlement can reach the millimeter level, and the effects of accurate and reliable analysis result and trueness and credibility are achieved.
Description
Technical Field
The application relates to a settlement evaluation method, in particular to a city infrastructure group settlement evaluation method, electronic equipment and a storage medium, and belongs to the technical field of infrastructure settlement evaluation methods.
Background
In recent years, with the rapid development of economic society of China, the urbanization process is accelerated continuously. Along with the progress of various urban constructions, urban infrastructures supporting the development of cities are continuously built and updated. On one hand, the construction engineering can change the geotechnical pressure around the construction site, cause the earth surface load to exceed the standard and cause the deformation of the soil body and the construction structure, and in turn can cause more serious structural deformation; on the other hand, the constructed urban infrastructure is influenced by other external environments during operation, and the structural state of the urban infrastructure at the initial construction stage is changed to a certain extent. In addition, some coastal cities have performed sea reclamation activities in recent years, which put higher demands on the construction and later use of these infrastructures. The support function of the infrastructure for urban development is often realized by people, such as urban railway stations, airports, subway hubs and the like. Therefore, once the operating conditions of the infrastructures are in trouble, huge economic losses can be caused and the life safety of urban residents can be even affected. Therefore, it is necessary to analyze the ground settlement conditions of the urban infrastructure in detail and to grasp the current development conditions of the urban infrastructure.
Synthetic Aperture Radar interferometry (instar) is an important ground settlement monitoring technology emerging in recent years. The interferometry is a measurement technique for converting an interference phase into a ground object at a high speed by performing operations such as removing a flat ground effect, calculating an elevation ambiguity, and unwrapping the phase on the interference phase. Because the phase difference is converted into the path difference of the imaging microwave, the Insar technology can often obtain the terrain, the landform and the small change of the earth surface of the monitored area, and the precision can reach the millimeter level. After years of application in the field of practical engineering, the Insar technology has been widely applied to urban ground settlement monitoring. Among them, the remote sensing satellite also has special SAR satellite. The Insar technology can obtain the ground settlement data in a specific time period by acquiring satellite images of specific time and specific monitoring areas. However, since only data of individual points are generally obtained, and how to obtain the urban infrastructure itself and even evaluate the ground settlement condition of partial areas of the city and the whole city from the change condition of the points, a targeted means and method are not available at present.
The conventional safety analysis theory is used, a corresponding risk evaluation index is established by means of a general scheme of a safety risk evaluation system, and the ground settlement risk evaluation is carried out by combining with the statistical data of the city. The common method is an analytic hierarchy process, risk evaluation indexes are established, different indexes are given through an expert guidance method to be distributed through weights, and the actual process is high in subjectivity. Meanwhile, since urban ground settlement is a complex process influenced by multiple factors, the evaluation process is achieved through subjective weight distribution, and large deviation is easily caused with actual conditions. In addition, some researchers utilize satellite images to calculate monitoring values of ground settlement, but due to the lack of effective theoretical support, ground settlement risk evaluation of the whole area is difficult to effectively support by actually abundant but scattered monitoring points, so that most of analysis is limited to qualitative and small amount of simple quantitative analysis, and more accurate analysis cannot be given according to actual conditions. Such analysis and conclusions do not provide effective reference and support for real-life manager decisions or actual management applications.
Disclosure of Invention
The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. It should be understood that this summary is not an exhaustive overview of the invention. It is not intended to determine the key or critical elements of the present invention, nor is it intended to limit the scope of the present invention. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is discussed later.
In view of the above, in order to solve the technical problems in the prior art, the invention provides an urban infrastructure group settlement evaluation method, an electronic device and a storage medium.
The first scheme comprises the following steps: the urban infrastructure group settlement evaluation method comprises the following steps:
s1, acquiring ground settlement parameters of urban infrastructure;
s2, dividing the infrastructure area;
s3, dividing city infrastructure groups in a cross-region manner;
and S4, obtaining the evaluation index of the ground settlement condition of the urban infrastructure group.
Preferably, the method for acquiring the ground settlement parameters of the urban infrastructure in S1 is as follows: the method comprises the following steps:
s11, screening Insar monitoring points related to infrastructure, wherein the method comprises the following steps: selecting a certain infrastructure as an S point, taking the S point as a center, taking the radius as r to draw a circle, selecting all monitoring points [ i1, i 2.. In ] in the circle, and calculating the distances from the S point to all monitoring points;
s12. In a monitoring point set [ i1, i 2.. In]Extracting monitoring deformation information of each monitoring point, and acquiring a surface deformation time sequence set [ S ] of the monitoring points within a certain time period i1 (t),S i2 (t),...S in (t)]Arranging the landmark deformation time sequences of the monitoring points in the order from small to large, and taking the value S at the 98 th percentile i (j)，i＝[i1,i2,...in]Carrying out normalization processing to ensure that the surface deformation value of the monitoring point i is less than or equal to 1:
s13, calculating all cross correlation function values in the time delay interval, wherein the cross correlation function values of the monitoring point i1 and the monitoring point i2 are as follows:
wherein, the first and the second end of the pipe are connected with each other,
respectively represent the time series S of the surface deformation i1 (t),S i2 (t + τ) mean value over the interval t =1,2.. L- τ, parameter τ representing the time series S of the deformation of the surface of the two monitoring points i1 (t),S i2 (t) a delay parameter, then S i1 (t),S i2 (t + τ), wherein the threshold value of the delay parameter is τ max I.e. with a time delay interval of [ - τ ] generated max ,τ max ]；
S14, calculating the correlation of all monitoring points in the monitoring point set [ i1, i 2.. In ] and the S point;
s15, acquiring a ground settlement parameter LS of the S point, and distributing the weight of the ground settlement parameter LS of each monitoring point based on the [ i1', i2',.. Im ' ] monitoring point set:
wherein, W S,i1' The weight assigned to the monitoring point i1' is shown, and the weights of other monitoring points are obtained according to the inverse proportion mode and are a weight set [ W S,i1' ,W S,i2' ,...W S,im' ]Obtaining the ground settlement parameter LS of the center point S of the infrastructure in the radius r range S ：
Preferably, the method for calculating the correlation between all monitoring points in the monitoring point set [ i1, i 2.. In ] and the S point in S14 is as follows: the method comprises the following steps:
s141, calculating the strength value of the correlation between the peripheral monitoring point and the S point based on all the cross-correlation function values in all the time delay intervals:
sequence [ X ] based on cross-correlation function values i1,i2 (-τ max ),...X i1,i2 (0),...X i1,i2 (τ max )]And calculating a correlation strength value PAR _ C between the original two monitoring point time sequences according to the following calculation formula:
wherein, max (X) i1,i2 (τ)) is the cross-correlation function X i1,i2 Maximum value of (τ), mean (X) i1,i2 (τ)) is the cross-correlation function X i1,i2 Average value of (τ), std (X) i1,i2 (τ)) is the cross-correlation function X i1,i2 (τ) variance;
s142, calculating the strength value of the correlation between the peripheral monitoring points and the S point in a mode of randomly disordering the surface deformation time sequence:
the surface deformation time series of the original monitoring points i1 and i2 are disturbed to obtain a new time series S i ' 1 (t),S i ' 2 (t) repeating S13-S141, repeating the process 1000 times to obtain a correlation intensity value sequence
[PAR_C 1 ,PAR_C 2 ,...PAR_C 1000 ]Calculating the average value of the sequence as the threshold value of the correlation strength value based on the correlation strength value sequence:
s143, calculating a correlation strength value PAR _ C according to the original time sequences of the monitoring points i1 and i2 i1,i2 And correlation strength value PAR _ C obtained after time series scrambling i ' 1,i2 Determines whether there is a significant correlation between the monitoring points:
wherein C (i 1, i 2) represents a significant correlation state value between monitoring points i1 and i2, and a value equal to 1 represents a significant correlation between monitoring points i1 and i 2; a value of 0 indicates that there is no significant correlation between monitoring point i1 and monitoring point i 2;
s144, executing S141-S143 on all monitoring points in the monitoring point set [ i1, i 2.. In ], and obtaining relevant monitoring point sets [ i1', i 2.. Im' ] of the S point in the range of the radius r.
Preferably, the method for dividing the infrastructure area S2 is: the method comprises the following steps:
s21, preliminarily dividing the urban infrastructure group, wherein the method comprises the following steps: the method comprises the following steps of re-dividing the result of a planned traffic cell in the traffic planning field, wherein the principle of re-dividing is to judge the area of a divided region, and re-dividing the current region when the divided region is more than 4% -5% of the total region; when the divided area is less than 2% -3% of the total area, combining the current area with other areas;
s22, the urban infrastructure group is divided again, and the method comprises the following steps: the method comprises the following steps:
s221, establishing a neighborhood radius r ', and selecting 5km-8km for the radius r';
s222, converting the ground settlement condition parameters LS of all infrastructures into object point numbers, and taking the minimum LS value of all infrastructures as a unit 1 and the quotient of the LS values of other infrastructures divided by the minimum LS value as the minimum point number; obtaining an object point number sequence, and taking the median of the object point number sequence as a minimum point value MinPts;
s224, establishing an upper limit value of the number of infrastructures in the infrastructure group, wherein the number of infrastructures in a single infrastructure group is not more than 5% of the total number;
s223, executing a DBSCAN algorithm to obtain a second clustering result;
s225, when the clustering result is the same as that of the primarily divided city infrastructure group, keeping the clustering result unchanged; and when the clustering result is different from the preliminary division of the urban infrastructure group, carrying out secondary division according to the clustering result.
Preferably, the method for dividing the urban infrastructure group across areas in S3 is: the method comprises the following steps:
s31, locating at TZ in the divided urban infrastructure network 2 Infrastructure Z in (1), considering allocation to TZ 2 N first-order adjacent zones TZ 21 ,TZ 22 …TZ 2N (ii) a Wherein TZ represents Traffic Zone, TZ 2 Representing a second traffic cell, TZ 2 First order adjacent representation with TZ 2 An adjacent traffic cell;
s32, satisfying the S coordinate D of the distance key infrastructure central point min Kilometer requirements, D min The value is referred to the city scale and the value range is [3,10](ii) a Parameter PAR of infrastructure Z Z Is assigned to TZ 21 The proportion of (A) is as follows:
wherein, the first and the second end of the pipe are connected with each other,
represents TZ 21 The corresponding attribute of (2).
Preferably, in S4, the method for obtaining the evaluation index of the ground settlement condition of the infrastructure group includes: the method comprises the following steps:
s41, adding the ground settlement parameters of all the infrastructures in the infrastructure group to obtain the ground settlement parameter value of a single infrastructure group;
s42, the ground settlement parameter indexes LS of G infrastructure groups have a set [ LS 1 ,LS 2 ,...LS G ]Setting basic parameter threshold values for time series of all facility groups to obtain a set [ LS ] 1 ',LS' 2 ,...LS' G ]Obtaining a multiple set [ T ] for all facilities groups 1 ,T 2 ,...T G ]Finally, for time t, the evaluation index of the ground settlement condition of the city whole infrastructure groupLS city (t) is:
preferably, the method for setting the basic parameter threshold value for the time series of all the facility groups is as follows:
s421, considering an actual redundancy principle, reserving a control space, and taking a percentage median of the sorted time sequence according to an actual time sequence;
s422, considering the actual functional characteristics and functional attributes of different facility groups, for the facility groups including key infrastructures, the threshold is reduced by a certain factor on the basis of the time series analysis result, and the threshold is specifically divided into three levels, namely 1.5 times of key, 1.2 times of key and 1 time of general.
Scheme II: an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the urban infrastructure group settlement evaluation method according to the first aspect when executing the computer program.
The third scheme is as follows: a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the urban infrastructure group settlement evaluation method of aspect one.
The invention has the following beneficial effects:
1. based on urban ground settlement Insar monitoring data, the monitoring precision of the data on urban ground settlement can reach the millimeter level, and the analysis result is accurate, reliable, real and credible;
2. the method comprises the steps of constructing an urban infrastructure group, dividing the urban infrastructure into a plurality of infrastructure groups by means of clustering in data analysis and machine learning, breaking the history of analyzing the settlement condition of a single data point in an isolated manner, combining the settlement conditions of a plurality of points, reflecting the urban infrastructure group and the ground settlement condition of the area where the urban infrastructure group is located more truly, and meanwhile, comprehensively managing each facility in the urban infrastructure group from the perspective of the infrastructure group;
3. the constructed settlement parameter LS of the infrastructure comprehensively considers the conditions of the infrastructure such as geographical conditions, random factors, relevance significance and the like, so that the constructed settlement parameter truly and effectively reflects the geological settlement condition of the infrastructure and the surrounding area thereof, and the misjudgment of the actual ground settlement condition due to the extreme value of individual monitoring points is avoided;
4. the settlement condition evaluation system of the multi-level city infrastructure group is constructed from the ground settlement condition evaluation parameters of a single static infrastructure, then to the infrastructure group and finally to the dynamic ground settlement condition evaluation system of the whole city level.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic process flow diagram;
FIG. 2 is a schematic flow chart of dividing city infrastructure groups based on a DBSCAN clustering algorithm.
Detailed Description
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following further detailed description of the exemplary embodiments of the present application with reference to the accompanying drawings makes it clear that the described embodiments are only a part of the embodiments of the present application, and are not exhaustive of all embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Example 1, the present embodiment will be described with reference to fig. 1 to 2, and the method for evaluating urban infrastructure group settlement includes the steps of:
s1, acquiring ground settlement parameters of urban infrastructure; for the existing urban infrastructure, the actual operation is often influenced by various parties. The ground settlement change condition of a single point location is often relatively random, and effective representation is difficult to achieve for large key city infrastructures such as airports, railway stations or stadiums. Meanwhile, the actual Insar data has a lot of monitoring point data, and the points are densely distributed and have a large range, so that an effective monitoring point set needs to be screened out and summarized into a ground settlement parameter, and the ground settlement condition of the infrastructure is effectively represented. The method comprises the following steps:
s11, screening Insar monitoring points related to infrastructure, wherein the method comprises the following steps: selecting a certain infrastructure, such as an airport, a railway station or a stadium, selecting an urban subway pivot point S, obtaining longitude and latitude values (LONGS, LATS) of the subway pivot point S from an urban map, taking the point S as a center, taking a circle range with a certain radius r, and selecting monitoring points in all circles. Wherein for a monitoring point i0 on the circumference. Similarly, the latitude and longitude (LONGi 0, LATi 0) of the monitoring point is taken from the city map, and then the distance DS, i0 between the points S and i0 is calculated by the following formula.
D s,i0 ＝R*arccos[cos(LAT S )*cos(LAT i0 )*cos(LONG S -LONG i0 )+sin(LAT i0 )sin(LAT S )]
Wherein, the radius R of the earth is 6371 km, and the result is particularly accurate to 0.01 m.
S12. In a monitoring point set [ i1, i 2.. In]Extracting monitoring deformation information of each monitoring point according to a multi-source high-resolution satellite radar image, wherein the monitoring deformation information comprises one of deformation data of a PS point and the PS point, a Persistent Scatterer-InSAR: PS-InSAR and an interference measurement technology of a permanent Scatterer synthetic aperture radar, and acquiring a surface deformation time sequence set [ S ] of the monitoring points within a certain time period i1 (t),S i2 (t),...S in (t)]Arranging the landmark deformation time sequences of the monitoring points from small to large, and taking the value S at the 98 th percentile i (j)，i＝[i1,i2,...in]Carrying out normalization processing to ensure that the surface deformation value of the monitoring point i is less than or equal to 1: the following is described for monitoring point i 1: take the value S at the 98 th percentile i1 (j) And carrying out normalization treatment:
after normalization processing, all the surface deformation values of the monitoring point i1 are less than or equal to 1.
Specifically, the other value processing methods are the same as those of i1.
Specifically, the normalization process mainly removes specific influence factors of each monitoring point individual, such as mountain positions, soil structures, urban functional area distribution and the like.
Wherein, the 98% quantile indicates that all sequences are arranged from small to large, and the 98 (quantile) is taken. For example, the 98 th digit is selected when 100 digits are sorted from small to large, and 98 is selected when the order is 1-100; if the 50 digits are ordered from 1 to 50, 98% is multiplied by 50=49, and the 49 th digit is taken. 98 quantites are positions that total 98 percent.
S13, calculating all cross correlation function values in the time delay interval, wherein the cross correlation function values of the monitoring point i1 and the monitoring point i2 are as follows:
wherein, the first and the second end of the pipe are connected with each other,
respectively represent the time series S of the surface deformation i1 (t),S i2 (t + τ) mean value over the interval t =1,2.. L- τ, parameter τ representing the time series S of surface deformation of two monitoring points i1 (t),S i2 (t) a delay parameter, then S i1 (t),S i2 (t + τ), wherein the threshold value of the delay parameter is τ max I.e. a time delay interval of [ - τ ] is generated max ,τ max ]；
S14, calculating the correlation of all monitoring points in the monitoring point set [ i1, i 2.. In ] and the S point, wherein the method comprises the following steps: the method comprises the following steps:
s141, calculating a strength value of the correlation between the peripheral monitoring point and the S point based on all the cross-correlation function values in all the time delay intervals:
sequence [ X ] based on cross-correlation function values i1,i2 (-τ max ),...X i1,i2 (0),...X i1,i2 (τ max )]And calculating a correlation strength value PAR _ C between the original two monitoring point time sequences according to the following calculation formula:
wherein, max (X) i1,i2 (τ)) is the cross-correlation function X i1,i2 Maximum value of (τ), mean (X) i1,i2 (τ)) is the cross-correlation function X i1,i2 Average value of (τ), std (X) i1,i2 (τ)) is the cross-correlation function X i1,i2 (τ) variance;
s142, calculating the strength value of the correlation between the peripheral monitoring points and the S point in a mode of randomly disordering the surface deformation time sequence:
the surface deformation time series of the original monitoring points i1 and i2 are disturbed to obtain a new time series S i ' 1 (t),S i ' 2 (t) repeating S13-S141, repeating the process 1000 times to obtain a correlation intensity value sequence
[PAR_C 1 ,PAR_C 2 ,...PAR_C 1000 ]Based on the sequence of correlation strength values, calculating the average of the sequence as a threshold value for the correlation strength value:
s143, calculating to obtain a correlation strength value PAR _ C according to the original time sequence of the monitoring points i1 and i2 i1,i2 And correlation strength value PAR _ C obtained after time series scrambling i ' 1,i2 Determines whether there is a significant correlation between the monitoring points:
wherein C (i 1, i 2) represents a significant correlation state value between monitor points i1 and i2, a value equal to 1 representing a significant correlation between monitor points i1 and i 2; a value of 0 indicates that there is no significant correlation between monitoring point i1 and monitoring point i 2;
s144, executing S141-S143 on all monitoring points in the monitoring point set [ i1, i 2.. In ], and obtaining relevant monitoring point sets [ i1', i 2.. Im' ] of the S point in the range of the radius r.
Collecting all monitoring points influencing the infrastructure central point S to obtain a ground settlement parameter LS of the point S; since the peripheral reference point of the point S to be initially selected is a circular area with the radius r selected, the distance factor is a significant influence that cannot be ignored in consideration of the interaction between the actually monitored point pairs. The influence of the distances from different monitoring points is different in magnitude. The weights of the points in the ground settlement parameter LS will be assigned. Consider that the interaction between pairs of monitoring points is inversely proportional to the actual distance magnitude. Therefore, when the ground settlement parameters of the S points are summarized, a distance set [ D ] from S exists for all the points in the set of points to be monitored S,i1' ,D S,i2' ,...D S,im' ]。
S15, acquiring the ground settlement parameters LS of the S points, and distributing the weight of the ground settlement parameters LS of each monitoring point based on the monitoring point set of [ i1', i2',.. Im ' ]:
wherein, W S,i1' The weight assigned to the monitoring point i1' is shown, and the weights of other monitoring points are obtained according to the inverse proportion mode and are a weight set [ W S,i1' ,W S,i2' ,...W S,im' ]Obtaining the ground settlement parameter LS of the center point S of the infrastructure in the radius r range S ：
S2, dividing infrastructure areas, constructing a static urban infrastructure group ground settlement condition evaluation system, acquiring ground settlement condition evaluation parameters of a single infrastructure based on S1, and as for actual conditions, the occurrence condition of first actual ground settlement is usually in block or strip continuous distribution; second, for the actual management hierarchy, unified management from the regional multi-facility level is required. Therefore, it is necessary to provide a system for evaluating the ground settlement condition describing the entire area from the viewpoint of the urban facility group. First, classification and analysis are performed based on multi-source monitoring and statistical data and functional attributes of each infrastructure to form a primary infrastructure group partition. And then generating a second clustering result by means of a clustering algorithm of the DBSCAN according to the actual ground settlement condition. And then, combining the two results, and further optimizing the primary division result, so that the final division result simultaneously meets the requirements of ground settlement practice and unified management.
The specific method comprises the following steps: the method comprises the following steps:
s21, preliminarily dividing the city infrastructure group, wherein the method comprises the following steps: re-dividing the result of the planned traffic cell in the traffic planning field according to the principle that the area of the divided area is judged, and when the divided area is larger than 4% -5% of the total area, re-dividing the current area; when the divided area is less than 2% -3% of the total area, combining the current area with other areas;
s22, the urban infrastructure group is divided again, and the method comprises the following steps: the method comprises the following steps:
s221, setting a neighborhood radius r ', and selecting 5km-8km for the radius r';
s222, converting the ground settlement condition parameters LS of all infrastructures into object point numbers, and taking the minimum LS value of all infrastructures as a unit 1 and the quotient of the LS values of other infrastructures divided by the minimum LS value as the minimum point number; obtaining an object point number sequence, and taking the median of the object point number sequence as a minimum point value MinPts;
s224, establishing an upper limit value of the number of infrastructures in the infrastructure group, wherein the number of infrastructures in a single infrastructure group is not more than 5% of the total number;
s223, executing a DBSCAN algorithm to obtain a second clustering result;
s225, when the clustering result is the same as the preliminarily divided urban infrastructure group, keeping the clustering result unchanged; and when the clustering result is different from the preliminary division of the urban infrastructure group, carrying out secondary division according to the clustering result.
S3, dividing urban infrastructure groups across areas, constructing a ground settlement condition evaluation system of dynamic urban infrastructure groups distributed across areas, and considering that only for the division of static urban infrastructure groups, some infrastructures with dynamic functions are used, such as urban airports, urban railway stations, urban large-scale stadiums (a stadium can hold more than 5 thousands of people, and other stadiums refer to the use attributes of the stadiums), important urban traffic hubs (daily passenger flow reaches a certain standard, and the specific city size and traffic travel scale are referred to), bridges, tunnels and important urban expressways. Since these infrastructures themselves affect a plurality of areas, it is considered to perform "cross-area division" on these areas, and finally obtain a dynamic system for evaluating the ground settlement conditions of the urban infrastructure group. The specific operation is to allocate the ground settlement parameters of the infrastructure to a plurality of actually affected infrastructure groups, the specific allocation process will refer to the action attributes of the infrastructure, and the specific attribute comparison table is shown in the corresponding attribute allocation table of the key infrastructure in table 1 below.
Table 1 key infrastructure corresponding attribute assignment table
The statistical mode of the passenger flow in the table is based on the statistical result of the passenger registered address area in a certain time period; two aspects of road network routes need to be considered, namely whether a region has a road directly connected with a key infrastructure or not, and then according to a statistical result of road traffic flow distribution in a certain period of time.
The specific method comprises the following steps: the method comprises the following steps:
s31, considering the cascade action mode of the actual region influence, and locating at TZ in the divided urban infrastructure network 2 Infrastructure Z in (1), considering allocation to TZ 2 N first-order adjacent zones TZ 21 ,TZ 22 …TZ 2N (ii) a Wherein TZ represents Traffic Zone, TZ 2 Representing a second traffic cell, TZ 2 First order adjacent representation with TZ 2 An adjacent traffic cell;
i.e. an infrastructure within a certain infrastructure group, assigns its attributes to other infrastructure groups that are first order adjacent to the infrastructure group. For example, an airport is a group of facilities where critical infrastructure is located, but the actual range of influence of the airport is not only within the group of facilities, but also has a large influence on other groups of facilities in the vicinity. It is necessary to assign the attributes of this infrastructure of the airport to other infrastructure groups in the neighborhood.
S32, satisfying the S coordinate D of the distance key infrastructure central point min Kilometer requirements, D min The value is referred to the city scale and the value range is [3,10](ii) a Parameter PAR of infrastructure Z Z Is assigned to TZ 21 The proportion of (A) is as follows:
wherein the content of the first and second substances,
represents TZ 21 The corresponding attribute of (2).
S4, obtaining a ground settlement condition evaluation index of the urban infrastructure group, further obtaining a ground settlement condition evaluation system of the whole city according to decision-making needs of a city manager and early warning management needs of actual urban infrastructure operation based on a dividing result of dividing the urban infrastructure group in a cross-region mode, wherein the method comprises the following steps: the method comprises the following steps:
s41, obtaining ground settlement parameter indexes of multiple infrastructures based on a single infrastructure group, and adding the ground settlement parameters of all infrastructures in the infrastructure group to obtain the ground settlement parameter value of the single infrastructure group;
and S42, obtaining a ground settlement condition evaluation system of the whole infrastructure group of the city, wherein the ground settlement condition evaluation system corresponds to the requirements of actual city managers and real management scenes. Ground settlement threatens personal and property safety of urban infrastructures and even urban residents, so that a ground settlement condition evaluation system of an integral urban infrastructure group is constructed based on ground settlement early warning and redundancy angles, and ground settlement parameter indexes LS of G infrastructure groups are integrated [ LS 1 ,LS 2 ,...LS G ]Setting basic parameter threshold value for time series of all facilities group to obtain set [ LS 1 ',LS' 2 ,...LS' G ]The setting principle of the parameter threshold is two:
1) The threshold setting needs to consider an actual redundancy principle, that is, a certain control space is reserved, and the percentage median of the sorted time series is generally selected according to an actual time series.
2) The threshold setting needs to consider the actual functional characteristics and functional attributes of different facility groups, and for the facility groups with important positions and more key infrastructures, the threshold is further reduced by a certain factor on the result of time series analysis. The method is divided into three levels, namely 1.5 times of key, 1.2 times of key and 1 time of common. The key infrastructure facilities include airports, large urban subway/public transportation hubs, railway stations, large substations, large urban stadiums, and the like.
Multiple sets T are obtained for all facility groups 1 ,T 2 ,...T G ]Finally, for time t, the city overall infrastructure group
Evaluation index LS of surface sedimentation condition city (t) is:
the evaluation index of the ground settlement condition of the whole city is obtained based on the angles of early warning and redundancy, and the evaluation and management of the ground settlement condition of the actual infrastructure and the decision support of a city manager can be effectively supported.
In embodiment 2, the computer device of the present invention may be a device including a processor and a memory, for example, a single chip microcomputer including a central processing unit. The processor is configured to implement the steps of the urban infrastructure group settlement evaluation method when executing the computer program stored in the memory.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Embodiment 3 computer-readable storage Medium embodiment
The computer readable storage medium of the present invention may be any form of storage medium that is read by a processor of a computer device, including but not limited to a non-volatile memory, a ferroelectric memory, and the like, and the computer readable storage medium has a computer program stored thereon, and when the processor of the computer device reads and executes the computer program stored in the memory, the steps of the above-mentioned city infrastructure group settlement evaluation method may be implemented.
The computer program comprises computer program code which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention has been disclosed in an illustrative rather than a restrictive sense, and the scope of the present invention is defined by the appended claims.
Claims (7)
1. The urban infrastructure group settlement evaluation method is characterized by comprising the following steps of:
s1, acquiring ground settlement parameters of urban infrastructure, wherein the method comprises the following steps: the method comprises the following steps:
s11, screening infrastructure-related Insar monitoringThe method comprises the following steps: selecting a certain infrastructure as S point, drawing a circle by taking the S point as a center and taking the radius as r, and selecting all monitoring points in the circle
Calculating the distances from the S point to all monitoring points;
s12, collecting monitoring points
The monitoring deformation information of each monitoring point is extracted, and the earth surface deformation time sequence set of the monitoring points in a certain time period is obtained
Arranging the surface deformation time sequences of the monitoring points from small to large, and taking the value at the 98 th sub-point
，
Performing normalization processing to make the monitoring pointsiThe surface deformation value is less than or equal to 1:
s13, calculating all cross correlation function values in the time delay interval, wherein the cross correlation function values of the monitoring point i1 and the monitoring point i2 are as follows:
wherein the content of the first and second substances,
respectively represent the time series of surface deformation
In that
Mean value over interval, parameter
Time series of surface deformation representing two monitoring points
The delay parameter of (1) is
Wherein the threshold value of the time delay parameter is
I.e. generating a time delay interval of
；
S14, calculating a monitoring point set
All monitoring points in the system have correlation with the point S;
s15, acquiring the ground settlement parameter LS of the S point based on
And (3) collecting monitoring points, and distributing the weight of the ground settlement parameter LS of each monitoring point:
wherein, the first and the second end of the pipe are connected with each other,
indicating points of surveillance
The assigned weights and the weights of other monitoring points are obtained according to the inverse proportion mode and are set as weights
Obtaining the ground settlement parameter of the center point S of the infrastructure in the radius r range
：
S2, dividing the infrastructure area, wherein the method comprises the following steps: the method comprises the following steps:
s21, preliminarily dividing the urban infrastructure group, wherein the method comprises the following steps: the method comprises the following steps of re-dividing the result of a planned traffic cell in the traffic planning field, wherein the principle of the re-division is that the area of a divided region is judged, and when the divided region is more than 4% of the total region, the current region is re-divided; when the divided area is less than 3% of the total area, merging the current area with other areas;
s22, the urban infrastructure group is divided again, and the method comprises the following steps: the method comprises the following steps:
s221, setting a neighborhood radius r ', and selecting 5km-8km for the radius r';
s222, converting the ground settlement condition parameters LS of all the infrastructures into object point numbers, and taking the minimum LS value of all the infrastructures as a unit 1 and the quotient of the LS values of other infrastructures divided by the minimum LS value as the minimum point number; obtaining an object point number sequence, and taking the median of the object point number sequence as a minimum point value MinPts;
s223, setting an upper limit value of the number of infrastructures in the infrastructure group, wherein the number of infrastructures in a single infrastructure group is not more than 5% of the total number;
s224, executing a DBSCAN algorithm to obtain a second clustering result;
s225, when the clustering result is the same as the preliminarily divided urban infrastructure group, keeping the clustering result unchanged; when the clustering result is different from the preliminary division of the urban infrastructure group, carrying out secondary division according to the clustering result;
s3, dividing city infrastructure groups in a cross-region manner;
and S4, obtaining the evaluation index of the ground settlement condition of the urban infrastructure group.
2. The urban infrastructure group settlement evaluation method according to claim 1, wherein the calculating the monitoring point set S14
The method for the correlation between all the monitoring points and the S point in (1) is as follows: the method comprises the following steps:
s141, calculating the strength value of the correlation between the peripheral monitoring point and the S point based on all the cross-correlation function values in all the time delay intervals:
sequences based on cross-correlation function values
Calculating the correlation strength value between the time series of the monitoring points i1 and i2
The calculation formula is as follows:
wherein the content of the first and second substances,
is a function of cross-correlation
The maximum value of (a) is,
is a function of cross-correlation
Is determined by the average value of (a),
is a function of cross-correlation
The variance of (a);
s142, calculating the strength value of the correlation between the peripheral monitoring points and the S point in a mode of randomly disordering the earth surface deformation time sequence:
the earth surface deformation time sequence of the monitoring points i1 and i2 is disturbed to obtain a new time sequence
Repeating the steps S13-S141, repeating the process 1000 times to obtain a correlation intensity value sequence
Calculating the average value of the sequence as the threshold value of the correlation strength value based on the correlation strength value sequence:
s143, calculating to obtain correlation strength values according to original time sequences of the monitoring points i1 and i2
And correlation strength values obtained after scrambling the time series
Determines whether there is a significant correlation between the monitoring pointsProperty:
wherein the content of the first and second substances,
a significant correlation state value between watch points i1 and i2 is represented, a value equal to 1 indicates that a significant correlation exists between watch points i1 and i 2; a value of 0 indicates that there is no significant correlation between monitoring point i1 and monitoring point i 2;
s144, collecting monitoring points
Performs S141-S143 to obtain a set of relevant monitor points of point S within radius r
。
3. The urban infrastructure group settlement evaluation method according to claim 2, wherein the method of dividing the urban infrastructure group across areas in S3 is: the method comprises the following steps:
s31, locating at TZ in the divided urban infrastructure network 2 Infrastructure Z in (1), considering allocation to TZ 2 N zones TZ adjacent to each other in the first order 21 , TZ 22 …TZ 2N (ii) a Wherein TZ represents Traffic Zone, TZ 2 Representing a second traffic cell, TZ 2 First order neighbor representation and TZ 2 An adjacent traffic cell;
s32, satisfying distance key infrastructure central point S coordinate D min Kilometer requirements, D min The value is referred to the city scale and the value range is [3,10]；
Parameter PAR of infrastructure Z Z Is assigned to TZ 21 The proportion of (A) is as follows:
wherein the content of the first and second substances,
represents TZ 21 Of the corresponding attribute.
4. The urban infrastructure group settlement evaluation method according to claim 3, wherein the method of obtaining the urban infrastructure group ground settlement condition evaluation index at S4 is: the method comprises the following steps:
s41, adding the ground settlement parameters of all the infrastructures in the infrastructure group to obtain the ground settlement parameter value of a single infrastructure group;
s42, the ground settlement parameter indexes LS of G infrastructure groups are integrated
Setting basic parameter threshold values for time series of all facility groups to obtain a set
All facilities get multiple sets
And finally, evaluating the ground settlement condition of the urban whole infrastructure group for the time t
Comprises the following steps:
5. the urban infrastructure group settlement evaluation method according to claim 4, wherein the method of setting the basic parameter threshold value for the time series of all the facility groups is:
s421, considering an actual redundancy principle, reserving a control space, and taking a percentage median of the sorted time sequence according to an actual time sequence;
s422, considering the actual functional characteristics and functional attributes of different facility groups, for the facility groups including key infrastructure, the threshold is reduced by a certain multiple on the result of time series analysis, and the facility groups are specifically divided into three levels, namely 1.5 times of key, 1.2 times of key and 1 time of general.
6. Electronic equipment, comprising a memory storing a computer program and a processor implementing the steps of the method for urban infrastructure group settlement evaluation according to any one of claims 1 to 5 when the computer program is executed by the processor.
7. Computer readable storage medium, having stored thereon a computer program, wherein the computer program, when being executed by a processor, implements the city infrastructure group settlement evaluation method of any one of claims 1 to 5.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110017762A (en) * | 2019-03-01 | 2019-07-16 | 四川省地质工程勘察院 | A method of utilizing Offset-Tracking technical monitoring Wriggle-changing Body deformation |
| CN112070129A (en) * | 2020-08-24 | 2020-12-11 | 首都师范大学 | Ground settlement risk identification method, device and system |
| CN112446559A (en) * | 2021-02-01 | 2021-03-05 | 中国测绘科学研究院 | Large-range ground subsidence space-time prediction method and system based on deep learning |
| CN113325419A (en) * | 2021-08-02 | 2021-08-31 | 湖南联智科技股份有限公司 | Pavement collapse detection method |
| CN113989197A (en) * | 2021-10-08 | 2022-01-28 | 武汉大学 | InSAR image registration method giving consideration to geometric and radiation characteristics |
| CN114547827A (en) * | 2022-04-26 | 2022-05-27 | 深圳市城市交通规划设计研究中心股份有限公司 | Infrastructure group running state evaluation method, electronic device and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108663017A (en) * | 2018-08-13 | 2018-10-16 | 伟志股份公司 | A kind of method of ground settlement along monitoring city underground |
-
2022
- 2022-07-14 CN CN202210823942.4A patent/CN114897454B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110017762A (en) * | 2019-03-01 | 2019-07-16 | 四川省地质工程勘察院 | A method of utilizing Offset-Tracking technical monitoring Wriggle-changing Body deformation |
| CN112070129A (en) * | 2020-08-24 | 2020-12-11 | 首都师范大学 | Ground settlement risk identification method, device and system |
| CN112446559A (en) * | 2021-02-01 | 2021-03-05 | 中国测绘科学研究院 | Large-range ground subsidence space-time prediction method and system based on deep learning |
| CN113325419A (en) * | 2021-08-02 | 2021-08-31 | 湖南联智科技股份有限公司 | Pavement collapse detection method |
| CN113989197A (en) * | 2021-10-08 | 2022-01-28 | 武汉大学 | InSAR image registration method giving consideration to geometric and radiation characteristics |
| CN114547827A (en) * | 2022-04-26 | 2022-05-27 | 深圳市城市交通规划设计研究中心股份有限公司 | Infrastructure group running state evaluation method, electronic device and storage medium |
Non-Patent Citations (4)
| Title |
|---|
| "Extraction and analysis of saline soil deformation in the Qarhan Salt Lake region (in Qinghai, China) by the sentinel SBAS-InSAR technique";WeiXiang等;《Geodesy and Geodynamics》;20220331;第13卷(第2期);第127-137页，全文 * |
| "Time-Series InSAR Technology for Ascending and Descending Orbital Images to Monitor Surface Deformation of the Metro Network in Chengdu";Bo Hu等;《 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing》;20211125;第12583-12595页，全文 * |
| "多时相InSAR技术及其在滑坡监测中的关键问题分析";孙倩等;《地理与地理信息科学》;20190531;第35卷(第3期);第37-43页，全文 * |
| "时序Insar技术对大连主城区沉降分析";杨帆等;《测绘工程》;20220531;第31卷(第3期);第61-66页，全文 * |
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