CN117574493A - Highway frozen soil range deformation identification method and system in permafrost region - Google Patents
Highway frozen soil range deformation identification method and system in permafrost region Download PDFInfo
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Abstract
The invention discloses a method and a system for identifying deformation of a frozen soil range of an expressway in a permafrost region, which relate to the technical field of topographic mapping, wherein if an abnormal region is more than expected, a detection region is divided into a risk region and a non-risk region, a plurality of mapping points are selected in the risk region, the risk region is mapped by an unmanned plane, and a mapping data set is established; establishing a deformation coefficient of a frozen soil layer in the risk area, and if the deformation coefficient does not exceed a deformation threshold currently, predicting the deformation coefficient by using a trained state prediction model to obtain a predicted value; predicting the deformation of the frozen soil layer after the deformation period, and screening out abnormal parameters as abnormal characteristics; matching the corresponding treatment schemes, performing simulation analysis on the treatment schemes by using the trained simulation model, and screening out the optimal treatment scheme as a reference scheme according to the analysis result. And predicting the deformation coefficient, and judging whether the frozen soil layer needs to be treated in advance according to the predicted value, so that the risk is avoided.
Description
Technical Field
The invention relates to the technical field of topographic mapping, in particular to a method and a system for identifying deformation of a frozen soil range of a highway in a permafrost region.
Background
Frozen soil refers to various rocks and soils containing ice at a temperature below zero degrees celsius. Generally, it can be classified into short-time frozen soil (hours/days to half months)/season frozen soil (half months to months) and permafrost soil (also called permanent frozen soil, refers to a frozen and unmelted soil layer lasting for two or more years). Frozen soil has rheological properties, and its long-term strength is far lower than that of instant strength. Because of these characteristics, construction of engineering structures in frozen soil areas must face two major hazards: frost heaving and thawing.
With the rise of temperature and the rise of ground water level caused by ice and snow melting, frozen soil is also gradually deformed, and the deformation can lead to uneven deformation of roadbed and influence the flatness of road.
Furthermore, as the volume of the frozen soil expands along with the reduction of the temperature, the roadbed and the steel rail built on the frozen soil can be jacked up due to the expansion of the frozen soil, and the frozen soil is melted and reduced in volume in summer, and the roadbed can be settled along with the expansion of the frozen soil. The repeated freezing and thawing can lead the roadbed to generate uneven deformation, so that the roadbed has the phenomena of wavy fluctuation and uneven height, and the stability and the safety of the running of the vehicle are seriously affected.
In general, the deformation of frozen soil can lead to the stability of roadbeds and side slopes to be reduced, thereby generating the problems of salivary ice, side slope slump and the like, and influencing the normal use and driving safety of roads.
In the Chinese patent of application publication No. CN109325311A, a deformation evaluation method of a large-scale roadbed in a permafrost region is disclosed, wherein the method comprises the steps of firstly, carrying out surface deformation elevation data acquisition on a road section to be evaluated, and establishing a gridded data set; then according to different roadbed deformation characteristics, sub-data sets representing four different deformation characteristics are split from the data set; and finally, according to four different road deformation characteristics, establishing a surface deformation evaluation index system, and calculating different deformation characteristic values to obtain a subentry deformation index, thereby finally obtaining the full-surface deformation index describing the road deformation characteristics.
In the application, the defects that the existing method for evaluating the road diseases in the frozen soil area cannot clear the deformation of the road surface, cannot accurately identify the cause of the road diseases in the frozen soil area and cannot accurately evaluate the deformation characteristic conditions of the road in the frozen soil area are overcome, and the method can evaluate the deformation proportion and the deformation degree of the road deformation in the frozen soil area in multiple years, which are multiple in changes and complicated, so that the scientific quantitative evaluation of the road bed in the frozen soil area in a large scale is realized.
However, in the above application, there is a certain disadvantage, for example, since deformation of the frozen soil layer is repeated, only deformation of the frozen soil layer is identified and evaluated, and it is insufficient to play a guiding role in management of the frozen soil layer, because it can find deformation existing at present of the frozen soil layer, but lack of prediction of potential deformation of the frozen soil layer is unfavorable for protecting highways located in the frozen soil region in advance.
Therefore, the invention provides a method and a system for identifying the deformation of the frozen soil range of the expressway in the permafrost region.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a method and a system for identifying the deformation of the frozen soil range of a highway in a permafrost region, wherein a detection region is divided into a risk region and a non-risk region, a plurality of mapping points are selected in the risk region, the risk region is mapped by an unmanned plane, and a mapping data set is established; establishing a deformation coefficient of a frozen soil layer in the risk area, and if the deformation coefficient does not exceed a deformation threshold currently, predicting the deformation coefficient by using a trained state prediction model to obtain a predicted value; predicting the deformation of the frozen soil layer after the deformation period, and screening out abnormal parameters as abnormal characteristics; matching the corresponding treatment schemes, performing simulation analysis on the treatment schemes by using the trained simulation model, and screening out the optimal treatment scheme as a reference scheme according to the analysis result, thereby solving the problems in the background technology.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: a deformation identification method for a frozen soil range of a highway in a permafrost region comprises the following steps: dividing the determined detection area into a plurality of sub-areas, respectively detecting the sub-areas, summarizing a plurality of groups of detection values to establish an environment data set, establishing environment coefficients Vo (t, ss) in the sub-areas by the environment data set, and if the abnormal area screened out according to the environment coefficients Vo (t, ss) is more than expected, sending an abnormal early warning to the outside; the environmental coefficient Vo (t, ss) is generated as follows: performing linear normalization processing on the surface temperature Dt, the soil layer water content Ts and the underground water level Ds, mapping corresponding data values into intervals [0,1], and then according to the following formula:
wherein, the parameter meaning is: n is a positive integer greater than 1, i=1, 2 … n, i being the number of each sub-region; weight systemThe number: f is 0 to or less 1 ≤1，0≤F 2 ≤1，0≤F 3 Not more than 1, and F 3 +F 2 +F 1 =1, saidIs the standard value of the surface temperature->Is the standard value of the water content of the soil layer, and is->Is the standard value of the underground water level;
after pre-abnormality early warning is received, a trained classifier is used for dividing a detection area into a risk area and a non-risk area, after a patrol path is planned, a plurality of mapping points are selected in the risk area, and an unmanned plane is used for mapping the risk area and establishing a mapping data set; establishing deformation coefficients De (b, b) of the frozen soil layer in the risk area by the mapping data set, if the deformation coefficients De (b, b) do not exceed the deformation threshold currently, sending out a prediction instruction, performing model training by using a multiple linear regression model, and predicting the deformation coefficients De (b, b) by using a state prediction model after training to obtain a predicted value;
if the obtained predicted value of the deformation coefficient De (b, b) is abnormal, establishing a modeling data set, training to generate a trained frozen soil deformation prediction model, predicting the frozen soil layer deformation after the deformation period by using the model, establishing a prediction parameter set, and screening abnormal parameters from the prediction parameter set to be used as abnormal characteristics;
according to the correspondence between the abnormal characteristics and the preset treatment schemes in the frozen soil layer treatment scheme library, matching the corresponding treatment schemes, performing simulation analysis on the corresponding treatment schemes by using a trained simulation model, and screening out the optimal treatment scheme as a reference scheme according to the analysis result.
Further, after the coverage area of the expressway is determined, setting detection distances, dividing the area, within the detection distances, of the expressway into detection areas, and dividing the detection areas into a plurality of sub-areas along the extending direction of the expressway after an electronic map of the detection areas is established through mapping and exploration data; and setting detection points in each subarea, detecting the environmental conditions in each subarea with a fixed detection period, and establishing an environmental data set according to detection results.
Further, the method for establishing the environment data set is as follows: detecting the surface temperature, the soil layer water content and the underground water level height at detection points in each detection period, outputting the maximum value of the data in each detection period as a detection value to respectively acquire the surface temperature Dt, the soil layer water content Ts and the underground water level Ds, and collecting and establishing an environment data set after acquiring a plurality of groups of detection values along a time axis;
establishing an environment coefficient Vo (t, ss) in the subarea by the environment data set, and determining the corresponding subarea as an abnormal area if the acquired environment coefficient Vo (t, ss) exceeds a condition threshold; if the proportion of the subarea marked as the abnormal area to all subareas exceeds the expected proportion, an abnormal early warning is sent to the outside.
Further, after receiving the pre-abnormality early warning, carrying out cluster analysis on the plurality of sub-areas, and dividing the plurality of sub-areas into a risk area and a non-risk area which are connected into pieces on an electronic map according to an analysis result; after the position information of a plurality of risk areas is acquired, a trained path planning model is combined with the position information to plan a patrol path covering the plurality of risk areas, so that the unmanned aerial vehicle patrol the plurality of risk areas along the patrol path;
remote sensing imaging and radar mapping are carried out on each risk area, corresponding mapping data are obtained, and the method specifically comprises the following steps: selecting a plurality of mapping points in the risk area, detecting the current depth and thickness of the frozen soil layer in the risk area at each mapping point by using a radar, and taking the ratio of the current depth and thickness as a depth ratio Db; imaging and measuring the risk area through digital close-range photography, comparing the risk area with historical data, and calculating to obtain the deformation of the frozen soil layer and obtain the deformation Tb of the soil layer; and after the depth ratio Db of each mapping point and the soil layer deformation Tb are summarized, a mapping data set is established.
Further, the deformation coefficient De (b, b) of the frozen soil layer in the risk area is established by mapping the data in the data set in the following specific manner; performing linear normalization processing on the depth ratio Db and the soil layer deformation Tb, mapping corresponding data values into intervals [0,1], and then according to the following formula:
wherein,is the historical average of depth ratio, +.>The weight coefficient is the historical average value of the soil layer deformation: beta is more than or equal to 0 and less than or equal to 1, alpha is more than or equal to 0 and less than or equal to 1,1 and alpha+beta is more than or equal to 1, i is more than or equal to 1,2, … n, n is a positive integer greater than 1, i is the number of each mapping point;
if the obtained deformation coefficient De (b, b) exceeds the deformation threshold, a processing instruction is sent out, and otherwise, a prediction instruction is sent out; taking data in the mapping data set and the environment data set as historical data, after a prediction instruction is received, combining the historical data in the historical data set, performing model training by using a multiple linear regression model through Spss, and predicting the deformation coefficient De (b, b) and obtaining a predicted value by using a state prediction model after training.
Further, if the obtained predicted value of the deformation coefficient De (b, b) is higher than the previous value and the higher proportion exceeds the preset proportion, generating a verification instruction, at least adopting state data of the frozen soil layer and environmental condition data of the frozen soil layer, and summarizing the data to form a modeling data set;
and (3) establishing a prediction model after adjusting a corresponding network architecture by using a Bp neural network, extracting partial data from a modeling data set as a test set and a training set, training and testing the prediction model, and outputting the trained prediction model as a frozen soil deformation prediction model.
Further, presetting a deformation period, predicting the deformation of the frozen soil layer after the deformation period by using a frozen soil deformation prediction model, obtaining prediction results of each parameter of the deformation of the frozen soil layer, summarizing a plurality of prediction results, and establishing a prediction parameter set;
and taking the historical average value of the parameters as a standard value, screening out a part of the predicted parameter set, the predicted value of which is larger than the standard value and the exceeding proportion of which is larger than the proportional threshold value, from the predicted parameter set after the proportional threshold value is preset, taking the part of the parameters as abnormal characteristics, and taking the degree of the parameters exceeding the standard value as the abnormal degree.
Further, collecting under a linear search matching line in a public channel, summarizing maintenance treatment schemes aiming at a frozen soil layer, establishing a treatment scheme library of the frozen soil layer, predicting the obtained abnormal characteristics of the frozen soil layer, matching one or more corresponding treatment schemes from the treatment scheme library according to the correspondence between the treatment schemes, and establishing a treatment scheme set after summarizing;
extracting part of data from the modeling data set, extracting features, selecting features, taking the extracted features as a test set and a training set, establishing a model through a machine learning algorithm, establishing and training the model, and outputting the tested model as a simulation model.
Further, performing simulation analysis on one or more treatment schemes in the treatment scheme set by using the trained simulation model, and acquiring a corresponding analysis result, wherein if only one treatment scheme in the treatment scheme set exists, the treatment scheme is used as a reference scheme; if more than one treatment schemes are adopted, the deformation coefficient De (b, b) of the frozen soil layer in the risk area after the treatment schemes are executed is obtained according to the analysis result, and the treatment scheme with the best effect is selected from a plurality of treatment schemes to serve as a reference scheme according to the value of the deformation coefficient De (b, b).
A permafrost region highway frozen soil range deformation identification system comprising:
the early warning unit divides the detection area into a plurality of sub-areas, establishes an environment data set and an environment coefficient after respectively detecting the detection area, screens the sub-areas, and sends out abnormal early warning if the screened abnormal area is more than expected;
the system comprises a mapping unit, a detection unit and a control unit, wherein the detection unit is used for dividing a detection area into a risk area and a non-risk area, a plurality of mapping points are selected in the risk area, the risk area is mapped by an unmanned aerial vehicle, and a mapping data set is established;
the first prediction unit is used for establishing a deformation coefficient of the frozen soil layer in the risk area, and if the deformation coefficient does not exceed the deformation threshold currently, a trained state prediction model is used for predicting the deformation coefficient to obtain a predicted value;
the second prediction unit is used for establishing a modeling data set and training to generate a frozen soil deformation prediction model if the predicted value of the deformation coefficient is abnormal, predicting the frozen soil layer deformation after the deformation period, and screening out abnormal parameters as abnormal characteristics;
the scheme matching unit is used for matching the corresponding treatment scheme according to the correspondence between the abnormal characteristics and the treatment scheme, carrying out simulation analysis on the treatment scheme by using the trained simulation model, and screening the optimal treatment scheme according to the analysis result to be used as a reference scheme.
(III) beneficial effects
The invention provides a method and a system for identifying the deformation of the frozen soil range of an expressway in a permafrost region, which have the following beneficial effects:
1. the mapping data set is established through the cooperation of different mapping means and is used for confirming the state of the frozen soil layer in the risk area, so that each risk area can be orderly processed according to the abnormal degree of the mapping data when needed, the priority of the processing is urgent, and the potential safety hazard is reduced with high efficiency.
2. After the deformation of the frozen soil layer is primarily identified, the degree of the change of the frozen soil layer is evaluated and judged, if the degree of the change exceeds the expected degree, timely processing is required, and the identification process is primarily completed; if the deformation coefficient De (b, b) is predicted by using the trained state prediction model, judging whether the frozen soil layer needs to be treated in advance according to the predicted value, and avoiding the risk.
3. Establishing a frozen soil deformation prediction model output by using a Bp network, predicting the change trend and the change process of a frozen soil layer by using the model, and acquiring abnormal characteristics from abnormal data if the frozen soil layer is abnormal; by predicting the change of the frozen soil layer, the change of each parameter of each frozen soil layer is obtained, and the prediction is performed on the basis of the frozen soil layer identification, so that the current potential risk can be known in advance.
4. After the recognition and prediction of the deformation of the frozen soil layer are completed, the corresponding treatment scheme can be prepared in advance according to the prediction result, so that the treatment can be timely performed when the actual deformation is generated or is about to be generated, and the potential safety hazard caused by the large-scale deformation of the frozen soil layer to the use of the expressway is reduced or avoided.
Drawings
FIG. 1 is a flow chart of a method for identifying the deformation of the frozen soil range of an expressway in a permafrost region according to the invention;
FIG. 2 is a schematic diagram of a system for recognizing the deformation of the frozen soil range of the expressway in the permafrost region;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a method for identifying deformation of a frozen soil range of an expressway in a permafrost region, which comprises the following steps:
dividing a determined detection area into a plurality of sub-areas, respectively detecting the sub-areas, summarizing a plurality of groups of detection values to establish an environment data set, establishing environment coefficients Vo (t, ss) in the sub-areas by the environment data set, and sending an abnormality early warning to the outside if the abnormal area screened according to the environment coefficients Vo (t, ss) is more than expected;
the first step comprises the following steps:
step 101, setting detection distances after determining coverage areas of highways, dividing areas within the detection distances from the highways into detection areas, and dividing the detection areas into a plurality of sub-areas along the extending direction of the highways after establishing an electronic map of the detection areas through mapping and exploration data;
setting detection points in each subarea, detecting the environmental conditions in each subarea with a fixed detection period, and establishing an environmental data set according to the detection result, wherein the establishment method of the environmental data set comprises the following steps:
setting a detection period, for example, 5 minutes as one detection period, detecting the surface temperature, the soil layer water content and the underground water level height at detection points in each detection period, outputting the maximum value of the data in each detection period as a detection value to respectively acquire the surface temperature Dt, the soil layer water content Ts and the underground water level Ds, and collecting and establishing an environment data set after acquiring a plurality of groups of detection values along a time axis;
step 102, building an environment coefficient Vo (t, ss) in the subarea from the environment data set, wherein the generation mode is as follows: performing linear normalization processing on the surface temperature Dt, the soil layer water content Ts and the underground water level Ds, mapping corresponding data values into intervals [0,1], and then according to the following formula:
wherein, the parameter meaning is: n is a positive integer greater than 1, i=1, 2 … n, i being the number of each sub-region; weight coefficient: f is 0 to or less 1 ≤1，0≤F 2 ≤1，0≤F 3 Not more than 1, and F 3 +F 2 +F 1 =1, saidIs the standard value of the surface temperature->Is the standard value of the water content of the soil layer, and is->Is the standard value of the underground water level;
after combining the historical data, on the basis of enabling the expressway to keep expected running conditions, presetting a standard value and an environmental condition threshold value of each item of environmental data, wherein if the acquired environmental coefficient Vo (t, ss) exceeds the condition threshold value, the environmental change is indicated to be beyond the expected value, which possibly affects the safe use of the expressway, so that the corresponding subarea is determined as an abnormal area; if the proportion of the sub-area marked as the abnormal area to all the sub-areas exceeds the expected value, for example, exceeds 30%, an abnormal warning is issued to the outside.
In use, the contents of steps 101 to 103 are combined:
before deformation identification is carried out on a frozen soil layer in a frozen soil area, an area to be identified and detected is firstly determined, environmental condition data in the area are detected to establish environmental coefficients Vo (t, ss), whether each delimited subarea is abnormal or not is judged according to the established environmental coefficients Vo (t, ss), if the abnormal subarea is generated, the fact that the current frozen soil area has a thawing potential risk is indicated if the abnormal subarea exceeds expectations, the structural parameters and the mechanical parameters of the frozen soil layer can be greatly changed, and timely processing is needed to reduce the influence degree of the environmental change on a highway on the frozen soil layer.
Step two, after the pre-abnormality early warning is received, a trained classifier is used for dividing a detection area into a risk area and a non-risk area, after a routing inspection path is planned, a plurality of mapping points are selected in the risk area, and an unmanned plane is used for mapping the risk area and establishing a mapping data set;
the second step specifically comprises the following steps:
step 201, after receiving pre-abnormality early warning, marking the position information of each abnormal region on an electronic map, using a trained classifier, combining the position information of the abnormal region and the values of the environment coefficients Vo (t, ss) of each sub-region, performing cluster analysis on a plurality of sub-regions, and dividing the plurality of sub-regions into a risk region and a non-risk region which are connected into pieces on the electronic map according to analysis results;
step 202, a path planning model is obtained through training of a sample data set by a path planning algorithm, after the position information of a plurality of risk areas is obtained, a routing inspection path covering the plurality of risk areas is planned by combining the trained path planning model with the position information of the risk areas, and an unmanned plane carrying remote sensing equipment and radar equipment is enabled to inspect the plurality of risk areas along the routing inspection path;
step 203, performing remote sensing imaging and radar mapping on each risk area, and acquiring corresponding mapping data, which specifically includes the following steps: selecting a plurality of mapping points in the risk area, uniformly distributing the plurality of mapping points, detecting the current depth and thickness of the frozen soil layer in the risk area at each mapping point through a radar, and taking the ratio of the current depth and thickness as a depth-to-thickness ratio Db;
imaging and measuring the risk area through digital close-range photography, comparing the risk area with historical data, and calculating and acquiring the deformation of the frozen soil layer according to the superposition degree of the current image and the historical data or other similar methods to acquire the deformation Tb of the soil layer; after the depth ratio Db of each mapping point and the soil layer deformation Tb are summarized, a mapping data set is established;
in use, the contents of steps 201 to 203 are combined:
after the abnormal early warning is received, the detection area is divided into a risk area and a non-risk area according to the values of the environment coefficients Vo (t, ss) of each sub-area, so that when the frozen soil layer needs to be processed, in order to reduce unnecessary workload and improve processing efficiency, only the risk area is subjected to further verification and identification temporarily, and the identification and processing efficiency is improved.
At this time, the mapping data set is established through the cooperation of different mapping means, and is used for confirming the state of the frozen soil layer in the dangerous area, so that each dangerous area can be orderly processed according to the abnormal degree of the mapping data when needed, the priority processing is urgent, and the potential safety hazard is reduced with high efficiency.
Thirdly, establishing deformation coefficients De (b, b) of the frozen soil layer in the risk area by the mapping data set, if the deformation coefficients De (b, b) do not exceed a deformation threshold currently, sending out a prediction instruction, performing model training by using a multiple linear regression model, and predicting the deformation coefficients De (b, b) by using a state prediction model after training to obtain a predicted value;
the third step specifically comprises the following steps:
step 301, establishing deformation coefficients De (b, b) of the frozen soil layer in the risk area by mapping data in the data set, in the following specific manner; performing linear normalization processing on the depth ratio Db and the soil layer deformation Tb, mapping corresponding data values into intervals [0,1], and then according to the following formula:
wherein,is the historical average of depth ratio, +.>The weight coefficient is the historical average value of the soil layer deformation: beta is more than or equal to 0 and less than or equal to 1, alpha is more than or equal to 0 and less than or equal to 1,1 and alpha+beta is more than or equal to 1, f is more than or equal to 1,2, … n, n is a positive integer greater than 1, and i is the number of each mapping point;
on the premise that the deformation degree of the frozen soil layer does not affect the quality of the expressway and the use state of the expressway, the deformation threshold is preset by combining the historical data, if the acquired deformation coefficient De (b, b) exceeds the deformation threshold, the deformation degree of the frozen soil layer is higher, timely processing is needed, and a processing instruction is sent out; otherwise, a prediction instruction is sent out;
step 302, taking the data in the mapping data set and the environmental data set as the historical data, after receiving the prediction instruction, combining the historical data in the historical data set, performing model training by using a multiple linear regression model through Spss, and after verification, measuring the prediction precision and fitting degree of the model, for example, by mean square error, R square value and the like, and optimizing the model according to the prediction precision and fitting degree which are difficult to reach the expectation, namely, when the model performance is difficult to reach the expectation,
the method specifically comprises the following steps: adjusting model parameters, adding or deleting characteristics and the like to improve the prediction performance and accuracy of the model, and generating and outputting a state conveying prediction model; specifically: the added characteristics are as follows: if the predictive performance of the model is insufficient, adding new features, introducing variables related to the response variables, and constructing the new features through principal component analysis;
delete irrelevant features: if more characteristics irrelevant to the response variables exist, the irrelevant characteristics can reduce the prediction capability of the model, and deletion is selected; feature selection: selecting the characteristic with the most close relation with the response variable from a plurality of characteristics by stepwise regression or Lasso regression so as to improve the prediction capability of the model; adjusting model parameters: in Lasso regression, the punishment degree of the model to the features is controlled by adjusting regularization parameter k, so that the prediction result of the model is influenced;
thus, the deformation coefficient De (b, b) is predicted and a predicted value is obtained by using the trained state prediction model;
in use, the contents of steps 301 and 302 are combined:
establishing deformation coefficients De (b, b) of the frozen soil layer in the risk area by data in the mapping data set, so that evaluation and judgment can be formed on the change degree of the frozen soil layer after the deformation of the frozen soil layer is primarily identified, if the change degree exceeds the expected value, timely processing is required, and the identification process is primarily completed; if the deformation coefficient De (b, b) is predicted by using the trained state prediction model, judging whether the frozen soil layer needs to be treated in advance according to the predicted value, and avoiding the risk.
If the obtained predicted value of the deformation coefficient De (b, b) is abnormal, establishing a modeling data set, training to generate a trained frozen soil deformation prediction model, predicting the frozen soil layer deformation after the deformation period by using the modeling data set, establishing a prediction parameter set, and screening abnormal parameters from the prediction parameter set to serve as abnormal characteristics;
the fourth step specifically comprises the following steps:
step 401, if the obtained predicted value of the deformation coefficient De (b, b) is higher than the previous value, and the higher proportion exceeds the preset proportion, for example, exceeds 10%, generating a verification command, at this time, at least adopting state data of the frozen soil layer, for example, thickness depth of the frozen soil layer, water content of the soil layer, and the like, and environmental condition data of the frozen soil layer, for example, ground temperature data, groundwater level data, air data, rainfall data, and the like; summarizing the data to form a modeling data set;
step 402, a Bp neural network is used, a prediction model is built after a corresponding network architecture is adjusted, partial data is extracted from a modeling data set to serve as a test set and a training set, the prediction model is trained and tested, and the trained prediction model is output as a frozen soil deformation prediction model;
presetting a deformation period, for example, 10 days, and predicting the deformation of the frozen soil layer after the deformation period by using a frozen soil deformation prediction model to obtain prediction results of various parameters of the deformation of the frozen soil layer, for example, deformation amount of the frozen soil layer, water content of the soil layer and the like; summarizing a plurality of prediction results, and establishing a prediction parameter set;
step 403, taking the historical average value of the parameter as a standard value, screening out a part of which the predicted value is larger than the standard value and the exceeding proportion is larger than the proportional threshold value from the predicted parameter set after the proportional threshold value is preset, taking the part of the parameter as an abnormal characteristic, and taking the degree of the parameter exceeding the standard value as an abnormal degree;
in use, the contents of steps 401 to 403 are combined;
when the predicted value of the deformation coefficient De (b, b) obtained through prediction is higher than the previous value, the risk of generating larger deformation of the frozen soil layer is larger, at the moment, a Bp network is used for establishing frozen soil deformation prediction model output, the model is used for predicting the change trend and the change process of the frozen soil layer, if the frozen soil layer is abnormal, abnormal characteristics are obtained in abnormal data; by predicting the change of the frozen soil layer, the change of each parameter of each frozen soil layer is obtained, and the prediction is performed on the basis of the frozen soil layer identification, so that the current potential risk can be known in advance.
Step five, matching corresponding treatment schemes according to the correspondence between abnormal characteristics and treatment schemes in a preset frozen soil layer treatment scheme library, performing simulation analysis on the corresponding treatment schemes by using a trained simulation model, and screening out an optimal treatment scheme as a reference scheme according to an analysis result;
the fifth step specifically comprises the following steps:
step 501, collecting under a public channel through a linear search matching line, summarizing maintenance treatment schemes aiming at a frozen soil layer, establishing a treatment scheme library of the frozen soil layer, predicting the obtained abnormal characteristics of the frozen soil layer, matching one or more corresponding treatment schemes from the treatment scheme library according to the correspondence between the treatment schemes, and establishing a treatment scheme set after summarizing;
step 502, extracting part of data from the modeling data set, extracting features, selecting features, taking the extracted features as a test set and a training set, establishing a model through a machine learning algorithm, establishing and training the model, comparing and verifying the model with an actual frozen soil layer, and adjusting model parameters and algorithms according to a comparison verification result to complete the test of the model, and outputting the tested model as a simulation model;
step 503, performing simulation analysis on one or more treatment schemes in the treatment scheme set by using the trained simulation model, and obtaining a corresponding analysis result, wherein if only one treatment scheme in the treatment scheme set exists, the treatment scheme is used as a reference scheme;
if more than one treatment schemes are adopted, according to analysis results, obtaining deformation coefficients De (b, b) of the frozen soil layer in the risk area after the treatment schemes are executed, and according to the values of the deformation coefficients De (b, b), screening the treatment scheme with the best effect from a plurality of treatment schemes as a reference scheme;
in use, the contents of steps 501 to 503 are combined:
after the change of the frozen soil layer is predicted, matching a corresponding treatment scheme according to the obtained abnormal characteristics, carrying out simulation analysis on whether the scheme is available, and judging whether the treatment scheme is available; when the treatment scheme has feasibility, the method is used as a reference scheme, if the matched treatment scheme is more than one, the treatment effect is screened out according to simulation analysis, so that after the recognition and prediction of the deformation of the frozen soil layer are completed, the method can be selected from the corresponding treatment schemes prepared in advance according to the prediction result, and the method can be used for timely processing when the actual deformation is generated or is about to be generated, so that the potential safety hazard caused by the large-scale deformation of the frozen soil layer to the use of the expressway is reduced or avoided.
Referring to fig. 2, the present invention provides a deformation identification system for a frozen soil range of an expressway in a permafrost region, comprising:
the early warning unit divides the detection area into a plurality of sub-areas, establishes an environment data set and an environment coefficient after respectively detecting the detection area, screens the sub-areas, and sends out abnormal early warning if the screened abnormal area is more than expected;
the system comprises a mapping unit, a detection unit and a control unit, wherein the detection unit is used for dividing a detection area into a risk area and a non-risk area, a plurality of mapping points are selected in the risk area, the risk area is mapped by an unmanned aerial vehicle, and a mapping data set is established;
the first prediction unit is used for establishing a deformation coefficient of the frozen soil layer in the risk area, and if the deformation coefficient does not exceed the deformation threshold currently, a trained state prediction model is used for predicting the deformation coefficient to obtain a predicted value;
the second prediction unit is used for establishing a modeling data set and training to generate a frozen soil deformation prediction model if the predicted value of the deformation coefficient is abnormal, predicting the frozen soil layer deformation after the deformation period, and screening out abnormal parameters as abnormal characteristics;
the scheme matching unit is used for matching the corresponding treatment scheme according to the correspondence between the abnormal characteristics and the treatment scheme, carrying out simulation analysis on the treatment scheme by using the trained simulation model, and screening the optimal treatment scheme according to the analysis result to be used as a reference scheme.
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.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. 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.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application.
Claims (10)
1. A deformation identification method for a frozen soil range of a highway in a permafrost region is characterized by comprising the following steps of: the method comprises the following steps:
dividing the determined detection area into a plurality of sub-areas, respectively detecting the sub-areas, summarizing a plurality of groups of detection values to establish an environment data set, establishing environment coefficients Vo (t, ss) in the sub-areas by the environment data set, and if the abnormal area screened out according to the environment coefficients Vo (t, ss) is more than expected, sending an abnormal early warning to the outside; the environmental coefficient Vo (t, ss) is generated as follows: performing linear normalization processing on the surface temperature Dt, the soil layer water content Ts and the underground water level Ds, mapping corresponding data values into intervals [0,1], and then according to the following formula:
wherein, the parameter meaning is: n is a positive integer greater than 1, i=1, 2 … n, i being the number of each sub-region; weight coefficient: f is 0 to or less 1 ≤1，0≤F 2 ≤1，0≤F 3 Not more than 1, and F 3 +F 2 +F 1 =1, saidIs the standard value of the surface temperature->Is the standard value of the water content of the soil layer, and is->Is the standard value of the underground water level;
after pre-abnormality early warning is received, a trained classifier is used for dividing a detection area into a risk area and a non-risk area, after a patrol path is planned, a plurality of mapping points are selected in the risk area, and an unmanned plane is used for mapping the risk area and establishing a mapping data set; establishing deformation coefficients De (b, b) of the frozen soil layer in the risk area by the mapping data set, if the deformation coefficients De (b, b) do not exceed the deformation threshold currently, sending out a prediction instruction, performing model training by using a multiple linear regression model, and predicting the deformation coefficients De (b, b) by using a state prediction model after training to obtain a predicted value;
if the obtained predicted value of the deformation coefficient De (b, b) is abnormal, establishing a modeling data set, training to generate a trained frozen soil deformation prediction model, predicting the frozen soil layer deformation after the deformation period by using the model, establishing a prediction parameter set, and screening abnormal parameters from the prediction parameter set to be used as abnormal characteristics;
according to the correspondence between the abnormal characteristics and the preset treatment schemes in the frozen soil layer treatment scheme library, matching the corresponding treatment schemes, performing simulation analysis on the corresponding treatment schemes by using a trained simulation model, and screening out the optimal treatment scheme as a reference scheme according to the analysis result.
2. The method for identifying the deformation of the frozen soil range of the expressway in the permafrost region according to claim 1, wherein the method comprises the following steps of: setting a detection distance after determining a coverage area of the expressway, dividing an area within the detection distance from the expressway into detection areas, and dividing the detection areas into a plurality of sub-areas with the same area along the extending direction of the expressway after establishing an electronic map of the detection areas through mapping and exploration data; and setting detection points in each subarea, detecting the environmental conditions in each subarea with a fixed detection period, and establishing an environmental data set according to detection results.
3. The method for identifying the deformation of the frozen soil range of the expressway in the permafrost region according to claim 2, wherein the method comprises the following steps of: the method for establishing the environment data set comprises the following steps:
detecting the surface temperature, the soil layer water content and the underground water level height at detection points in each detection period, outputting the maximum value of the data in each detection period as a detection value to respectively acquire the surface temperature Dt, the soil layer water content Ts and the underground water level Ds, and collecting and establishing an environment data set after acquiring a plurality of groups of detection values along a time axis;
establishing an environment coefficient Vo (t, ss) in the subarea by the environment data set, and determining the corresponding subarea as an abnormal area if the acquired environment coefficient Vo (t, ss) exceeds a condition threshold; if the proportion of the subarea marked as the abnormal area to all subareas exceeds the expected proportion, an abnormal early warning is sent to the outside.
4. A method for identifying the deformation of the frozen soil range of an expressway in a permafrost region according to claim 3, wherein the method comprises the following steps: after receiving the pre-abnormality early warning, carrying out cluster analysis on the plurality of sub-areas, and dividing the plurality of sub-areas into a risk area and a non-risk area which are connected into pieces on an electronic map according to an analysis result; after the position information of a plurality of risk areas is acquired, a trained path planning model is combined with the position information to plan a patrol path covering the plurality of risk areas, so that the unmanned aerial vehicle patrol the plurality of risk areas along the patrol path;
remote sensing imaging and radar mapping are carried out on each risk area, corresponding mapping data are obtained, and the method specifically comprises the following steps: selecting a plurality of mapping points in the risk area, detecting the current depth and thickness of the frozen soil layer in the risk area at each mapping point by using a radar, and taking the ratio of the current depth and thickness as a depth ratio Db;
imaging and measuring the risk area through digital close-range photography, comparing the risk area with historical data, and calculating to obtain the deformation of the frozen soil layer and obtain the deformation Tb of the soil layer; and after the depth ratio Db of each mapping point and the soil layer deformation Tb are summarized, a mapping data set is established.
5. The method for identifying the deformation of the frozen soil range of the expressway in the permafrost region according to claim 1, wherein the method comprises the following steps of: establishing a deformation coefficient De (b, b) of the frozen soil layer in the risk area by mapping data in the data set in the following specific mode; performing linear normalization processing on the depth ratio Db and the soil layer deformation Tb, mapping corresponding data values into intervals [0,1], and then according to the following formula:
wherein,is the historical average of depth ratio, +.>The weight coefficient is the historical average value of the soil layer deformation: beta is more than or equal to 0 and less than or equal to 1, alpha is more than or equal to 0 and less than or equal to 1,1 and alpha+beta is more than or equal to 1, i is more than or equal to 1,2, … n, n is a positive integer greater than 1, i is the number of each mapping point;
if the obtained deformation coefficient De (b, b) exceeds the deformation threshold, a processing instruction is sent out, and otherwise, a prediction instruction is sent out;
taking data in the mapping data set and the environment data set as historical data, after a prediction instruction is received, combining the historical data in the historical data set, performing model training by using a multiple linear regression model through Spss, and predicting the deformation coefficient De (b, b) and obtaining a predicted value by using a state prediction model after training.
6. The method for identifying the deformation of the frozen soil range of the expressway in the permafrost region according to claim 1, wherein the method comprises the following steps of: if the obtained predicted value of the deformation coefficient De (b, b) is higher than the previous value and the higher proportion exceeds the preset proportion, generating a verification instruction, at least adopting the state data of the frozen soil layer and the environmental condition data of the frozen soil layer, and summarizing the data to form a modeling data set;
and (3) establishing a prediction model after adjusting a corresponding network architecture by using a Bp neural network, extracting partial data from a modeling data set as a test set and a training set, training and testing the prediction model, and outputting the trained prediction model as a frozen soil deformation prediction model.
7. The method for identifying the deformation of the frozen soil range of the expressway in the permafrost region according to claim 6, wherein the method comprises the following steps of: presetting a deformation period, predicting the deformation of the frozen soil layer after the deformation period by using a frozen soil deformation prediction model, acquiring prediction results of each parameter of the deformation of the frozen soil layer, summarizing a plurality of prediction results, and establishing a prediction parameter set;
and taking the historical average value of the parameters as a standard value, screening out a part of the predicted parameter set, the predicted value of which is larger than the standard value and the exceeding proportion of which is larger than the proportional threshold value, from the predicted parameter set after the proportional threshold value is preset, taking the part of the parameters as abnormal characteristics, and taking the degree of the parameters exceeding the standard value as the abnormal degree.
8. The method for identifying the deformation of the frozen soil range of the expressway in the permafrost region according to claim 7, wherein the method comprises the following steps of: collecting under a linear search matching line in a public channel, summarizing maintenance treatment schemes aiming at a frozen soil layer, establishing a treatment scheme library of the frozen soil layer, predicting the obtained abnormal characteristics of the frozen soil layer, matching one or more corresponding treatment schemes from the treatment scheme library according to the correspondence of the treatment schemes, and establishing a treatment scheme set after summarizing;
extracting part of data from the modeling data set, extracting features, selecting features, taking the extracted features as a test set and a training set, establishing a model through a machine learning algorithm, establishing and training the model, and outputting the tested model as a simulation model.
9. The method for identifying the deformation of the frozen soil range of the expressway in the permafrost region according to claim 8, wherein the method comprises the following steps of: performing simulation analysis on one or more treatment schemes in the treatment scheme set by using the trained simulation model, acquiring corresponding analysis results, and taking the treatment scheme as a reference scheme if only one treatment scheme in the treatment scheme set exists; if more than one treatment schemes are adopted, the deformation coefficient De (b, b) of the frozen soil layer in the risk area after the treatment schemes are executed is obtained according to the analysis result, and the treatment scheme with the best effect is selected from a plurality of treatment schemes to serve as a reference scheme according to the value of the deformation coefficient De (b, b).
10. A permafrost region highway frozen soil range deformation identification system, to which the method of any one of claims 1 to 9 is applied, characterized in that: comprising the following steps:
the early warning unit divides the detection area into a plurality of sub-areas, establishes an environment data set and an environment coefficient after respectively detecting the detection area, screens the sub-areas, and sends out abnormal early warning if the screened abnormal area is more than expected;
the system comprises a mapping unit, a detection unit and a control unit, wherein the detection unit is used for dividing a detection area into a risk area and a non-risk area, a plurality of mapping points are selected in the risk area, the risk area is mapped by an unmanned aerial vehicle, and a mapping data set is established;
the first prediction unit is used for establishing a deformation coefficient of the frozen soil layer in the risk area, and if the deformation coefficient does not exceed the deformation threshold currently, a trained state prediction model is used for predicting the deformation coefficient to obtain a predicted value;
the second prediction unit is used for establishing a modeling data set and training to generate a frozen soil deformation prediction model if the predicted value of the deformation coefficient is abnormal, predicting the frozen soil layer deformation after the deformation period, and screening out abnormal parameters as abnormal characteristics;
the scheme matching unit is used for matching the corresponding treatment scheme according to the correspondence between the abnormal characteristics and the treatment scheme, carrying out simulation analysis on the treatment scheme by using the trained simulation model, and screening the optimal treatment scheme according to the analysis result to be used as a reference scheme.
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