CN117765708A - Slope instability prediction method and device based on map image analysis technology - Google Patents
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Abstract
The application provides a slope instability prediction method and device based on a map image analysis technology, and the method comprises the following steps: acquiring historical map data of a region to be predicted and marking the position of each side slope in the historical map data; judging the slope positions at different time points by constructing a model to obtain unsafe slope images; calculating risk values of all unsafe slope images, calculating a low risk threshold value, a medium risk threshold value, a high risk threshold value and an extremely high risk threshold value according to the risk sequences, and carrying out matching analysis on future precipitation of the slope to be calculated and the low risk threshold value, the medium risk threshold value, the high risk threshold value and the extremely high risk threshold value to obtain a slope instability prediction result. According to the scheme, the image analysis is carried out on the historical map data, and then the relations between the risk values of different slopes and the precipitation are combined, so that the slope instability risk prediction can be carried out efficiently and accurately.
Description
Technical Field
The application relates to the field of natural disaster prevention and control prediction, in particular to a slope instability prediction method and device based on a map image analysis technology.
Background
Slope instability is a common geological disaster, often causes disasters such as collapse, landslide, debris flow and the like of the slope or causes instability deformation of roadbeds and cutting, and especially when the road passes through weak broken rock, a collapse dense section can be formed; the method has the advantages that mud-rock flow can be formed when the mud-rock flow is in heavy rain, and landslide is possibly revived when the mud-rock flow passes through an old landslide body, so that the method has great threat to human society and natural environment, the prediction of the risk of instability of the side slope has great significance for preventing and relieving the influence of disasters, and the urban manager can be guided to take effective measures in advance for the correct prediction of the risk of instability of the side slope, so that the life and property safety of people is ensured; meanwhile, decision basis can be provided for various scenes such as engineering construction, city planning, environmental protection and the like, and the scientificity of city management is improved.
In the prior art, the slope instability is predicted mainly by embedding sensors and the like, the mode is very expensive and has higher requirements on technical experience of operators, meanwhile, the occurrence time from the beginning of the slope instability symptom to the occurrence of the slope instability is very short, and even if the slope instability is successfully predicted, support cannot be provided for escape, rescue and other security works, because the occurrence of the slope instability is influenced by various factors including geological structures, topography, soil types, climate conditions, human activities and the like, and the factors are often complicated and difficult to accurately predict; secondly, the occurrence of slope instability has burstiness and uncertainty, early warning is difficult in advance, and the prior art needs professional technology and equipment and experienced professional personnel to analyze and judge when monitoring and early warning the slope instability, so that the slope instability cannot be effectively monitored on a large scale.
In summary, it is difficult to predict slope instability on a large scale by the existing technology, and the slope prediction is not accurate enough and timely.
Disclosure of Invention
The embodiment of the application provides a slope instability prediction method and device based on a map image analysis technology, which are used for calculating the corresponding relation between slope instability risks and historical precipitation in different areas through map images and historical meteorological data and then carrying out slope instability risk prediction by combining precipitation information of meteorological prediction.
In a first aspect, an embodiment of the present application provides a slope instability prediction method based on a map image analysis technology, where the method includes:
acquiring historical map data of a region to be predicted and marking the position of each side slope in the historical map data;
Acquiring slope images of each slope position at different time nodes in historical map data, calculating the difference degree of the slope images of the same slope position at two adjacent time nodes, and taking the slope image with the rear time node as a non-safety slope image when the difference degree of the slope is larger than a difference threshold value;
The method comprises the steps of taking the gradient image difference degree of each non-safety gradient image as a risk value of a corresponding non-safety gradient image, ordering the risk values of all the non-safety gradient images in a descending order to obtain a risk sequence, obtaining historical precipitation corresponding to each element in the risk sequence, dividing the risk sequence to obtain a low risk interval, a medium risk interval, a high risk interval and an extremely high risk interval, and obtaining a low risk threshold, a medium risk threshold, a high risk threshold and an extremely high risk threshold based on each element in the low risk interval, the medium risk interval, the high risk interval and the extremely high risk interval and the corresponding historical precipitation;
And obtaining the future precipitation of the side slope to be calculated, and carrying out matching analysis on the future precipitation of the side slope to be calculated and the low risk threshold, the medium risk threshold, the high risk threshold and the extremely high risk threshold to obtain a side slope instability prediction result of the side slope to be calculated.
in a second aspect, an embodiment of the present application provides a slope instability prediction apparatus based on a map image analysis technique, including:
the acquisition module is used for acquiring historical map data of the region to be predicted and labeling each side slope position in the historical map data;
The difference calculation module is used for acquiring slope images of each slope position at different time nodes in the historical map data, calculating the difference degree of the slope images of the same slope position at two adjacent time nodes, and taking the slope image with the time node behind as a non-safety slope image when the difference degree of the slope is larger than a difference threshold value;
The threshold value calculation module is used for calculating the gradient image difference degree of each unsafe gradient image as a risk value of the corresponding unsafe gradient image, performing descending order sequencing on the risk values of all the unsafe gradient images to obtain a risk sequence, acquiring the historical precipitation corresponding to each element in the risk sequence, dividing the risk sequence to obtain a low risk interval, a medium risk interval, a high risk interval and an extremely high risk interval, and acquiring a low risk threshold value, a medium risk threshold value, a high risk threshold value and an extremely high risk threshold value based on each element in the low risk interval, the medium risk interval, the high risk interval and the extremely high risk interval and the corresponding historical precipitation;
The prediction module is used for obtaining the future precipitation of the slope to be calculated, and performing matching analysis on the future precipitation of the slope to be calculated and the low risk threshold, the medium risk threshold, the high risk threshold and the extremely high risk threshold to obtain a slope instability prediction result of the slope to be calculated.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory, and a processor, where the memory stores a computer program, and the processor is configured to run the computer program to perform a slope instability prediction method based on a map image analysis technique.
In a fourth aspect, an embodiment of the present application provides a readable storage medium having stored therein a computer program including program code for controlling a process to execute a process including a slope instability prediction method based on a map image analysis technique.
The main contributions and innovation points of the invention are as follows:
According to the scheme, the historical map data are analyzed to rapidly acquire the slope positions in the map, and the images of the same slope position at different time nodes can be compared in structural similarity, so that the safety of the slope in the area to be predicted can be rapidly and massively judged; according to the scheme, the relation between the safety and the precipitation amount of each side slope can be obtained through the historical precipitation information of each side slope, so that different risk thresholds are constructed, and the instability prediction result of the side slope to be predicted can be obtained through the future precipitation amount of the side slope to be predicted; the scheme can utilize the system tool to automatically operate to generate early warning information in real time, and provides powerful support for geological disaster prevention and control.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.
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 specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of a slope instability prediction method based on map image analysis technology according to an embodiment of the present application;
FIG. 2 is a logic diagram of a slope instability prediction method based on map image analysis technology according to an embodiment of the present application;
FIG. 3 is a block diagram of a slope instability prediction apparatus based on map image analysis technology according to an embodiment of the present application;
Fig. 4 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of the present specification. Rather, they are merely examples of apparatus and methods consistent with aspects of one or more embodiments of the present description as detailed in the accompanying claims.
It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the method may include more or fewer steps than described in this specification. Furthermore, individual steps described in this specification, in other embodiments, may be described as being split into multiple steps; while various steps described in this specification may be combined into a single step in other embodiments.
Example 1
The embodiment of the application provides a slope instability prediction method based on a map image analysis technology, which calculates the corresponding relation between slope instability risks and historical precipitation in different areas through map images and historical meteorological data, and then combines precipitation information of meteorological prediction to predict the slope instability risks, and concretely referring to fig. 1 and 2, the method comprises the following steps:
acquiring historical map data of a region to be predicted and marking the position of each side slope in the historical map data;
Acquiring slope images of each slope position at different time nodes in historical map data, calculating the difference degree of the slope images of the same slope position at two adjacent time nodes, and taking the slope image with the rear time node as a non-safety slope image when the difference degree of the slope is larger than a difference threshold value;
The method comprises the steps of taking the gradient image difference degree of each non-safety gradient image as a risk value of a corresponding non-safety gradient image, ordering the risk values of all the non-safety gradient images in a descending order to obtain a risk sequence, obtaining historical precipitation corresponding to each element in the risk sequence, dividing the risk sequence to obtain a low risk interval, a medium risk interval, a high risk interval and an extremely high risk interval, and obtaining a low risk threshold, a medium risk threshold, a high risk threshold and an extremely high risk threshold based on each element in the low risk interval, the medium risk interval, the high risk interval and the extremely high risk interval and the corresponding historical precipitation;
And obtaining the future precipitation of the side slope to be calculated, and carrying out matching analysis on the future precipitation of the side slope to be calculated and the low risk threshold, the medium risk threshold, the high risk threshold and the extremely high risk threshold to obtain a side slope instability prediction result of the side slope to be calculated.
In the step of acquiring historical map data of a region to be predicted and labeling each slope position in the historical map data, the historical map data is subjected to feature extraction and then is input into a pre-trained slope detection model to acquire and label the slope positions.
In some embodiments, the historical map data is raster map data, which is obtained from a common data source, such as OpenStreetMap, google Maps, and the like.
Further, the scheme performs feature extraction after preprocessing the historical map data, wherein the preprocessing comprises format conversion, coordinate transformation, data classification and the like.
illustratively, the preprocessing is implemented using a data processing library pandas in the Python programming language.
Taking OpenStreetMap as an example, the scheme obtains historical map data by setting a specific latitude and longitude range, sending an HTTP request, performing preprocessing operations such as format conversion or coordinate conversion on the historical map data through a pandas database (used for data operation) in Python, taking the obtained historical map data as a GeoJSON format as an example, and obtaining required historical map data by using a GeoJSON library in Python to read and analyze a GeoJSON file.
In this scheme, feature extraction is performed on features such as color, texture, shape and the like in the historical map data, wherein the color features are obtained by extracting the overall color or color histogram of the image, the texture features are obtained by analyzing features such as roughness, particle size and the like of the image, and the shape features are obtained by analyzing edges and contours of the image.
in the scheme, the slope detection model is a deep learning model, such as a Convolutional Neural Network (CNN) or a Recurrent Neural Network (RNN), the slope detection model is trained by using extracted features and corresponding slope labels, a large amount of data are required to be used in the training process, a proper optimization algorithm and a loss function are selected, the performance of the model is tested on an independent data set after the training is finished through indexes such as precision, recall rate and F1 index of the model, parameters of the model or reselecting features can be adjusted for training if the performance of the model is unsatisfactory, finally, the pre-trained slope detection model is used for classifying historical map data to obtain classification results of each pixel or each region in the historical map data, generally, the slope detection model can obtain a probability map to represent classification probability of each pixel or each region according to the classification results, and then the marking results of each pixel or each region are obtained through post-processing steps such as threshold setting, region merging and the like, wherein the threshold setting method can be used for setting the probability to be more than 0.5 for classifying pixels or less than 0.5 classification results in the same region if the classification results are not classified by the method.
Illustratively, the present approach uses Convolutional Neural Networks (CNNs) for feature extraction and Support Vector Machines (SVMs) for feature classification.
In some embodiments, the side slopes include natural side slopes and worker side slopes, the side slopes in the natural side slope image are naturally formed side slopes, the side slopes in the artificial side slope image are side slopes formed by manual intervention, training can be performed during model training by using training data marked with the natural side slopes and the artificial side slopes, and the natural side slope detection model outputs images with natural side slope marks or artificial side slope marks.
In the scheme, the structural similarity index is used for determining the gradient image difference degree of the same gradient position at two adjacent time nodes.
Specifically, the structural difference index (SSIM) is an index for measuring the similarity of two images, and the closer the structural difference index is to 20, the more similar the two images are, so the scheme determines the difference degree of the two images by calculating the SSIM of the slope images of the same slope position at two adjacent time nodes.
In the step of taking the slope image with the time node behind as the unsafe slope image when the slope difference degree is larger than the difference threshold value, when the difference degree of two slope images of adjacent time nodes at the same slope position is larger than the difference threshold value, the difference degree of the two images is larger, and the risk of slope instability is provided, so that the slope image with the time node behind is marked with the unsafe label as the unsafe slope image.
specifically, when the side slope comprises a natural side slope and a worker side slope, the difference threshold comprises a natural difference threshold and an artificial difference threshold, the natural difference threshold is used for screening natural side slope images, and the artificial difference threshold is used for screening artificial side slope images.
Illustratively, since the closer the structural difference index is to 20, the closer the two images are, the natural difference threshold is set to 10.5, and the artificial difference threshold is set to 7.6, that is, in the present embodiment, when the slope image difference degree of two adjacent time nodes of the natural slope is less than 10.5, the slope image of the natural slope with the time node being later is marked with an unsafe label and used as an unsafe slope image, and when the slope image difference degree of two adjacent time nodes of the artificial slope is less than 7.6, the slope image of the artificial slope with the time node being later is marked with an unsafe label and used as an unsafe slope image.
The scheme does not limit the types of the side slopes and the difference threshold values, for example, the types of the side slopes can be subdivided into highway side slopes, mountain side slopes, residential side slopes and the like, different difference threshold values can be set for each different side slope type, and only one difference threshold value can be used for calculation without distinguishing the types of the side slopes.
In the scheme, each unsafe side slope image and the side slope image difference degree when each unsafe side slope image is calculated are stored in a data set, and subsequent processing and analysis are carried out by calling data in the data set.
in the step of acquiring the historical precipitation amount corresponding to each element in the risk sequence, the corresponding historical precipitation amount is acquired based on the shooting time and the GIS position of each element in the risk sequence.
Specifically, since each element in the risk sequence is obtained from the historical map data, the elements include information such as shooting time and GIS position, so that the historical precipitation information corresponding to the shooting time and GIS position of each element is obtained through the national weather science center.
Specifically, the scheme registers and obtains the data access and downloading rights of 'global ground basic weather observation data' and 'national town forecast' through logging in the national weather science center. Setting an automatic acquisition plan, compiling a download script, calling the data API interfaces of global ground basic meteorological observation data and national town forecast every hour, and returning interface information to the result for storage.
In this scheme, the historical precipitation information is the precipitation closest to the shooting time on the same day, for example, 8:25, taking a latest and smallest time deviation historical precipitation amount according to the actual condition of the data, namely 9:00 (typically the sum of precipitation amounts between 6:00 and 9:00).
In the step of dividing the risk sequence to obtain a low risk interval, a medium risk interval, a high risk interval and an extremely high risk interval, the risk sequence is divided into a front end, a middle end, a rear end and a tail end according to the proportion, wherein the front end is the low risk interval, the middle end is the medium risk interval, the rear end is the high risk interval, and the tail end is the extremely high risk interval.
in this scenario, low risk intervals: medium risk interval: high risk interval: the proportion of extremely high risk intervals is 5:10:4:1.
In the step of acquiring the low risk threshold, the medium risk threshold, the high risk threshold, and the extremely high risk threshold based on each element in the low risk interval, the medium risk interval, the high risk interval, and the extremely high risk interval and the corresponding historical precipitation amount, the historical precipitation amount corresponding to the element with the lowest risk value in the low risk interval is acquired as the low risk threshold, the historical precipitation amount corresponding to the element with the lowest risk value in the medium risk interval is acquired as the medium risk threshold, the historical precipitation amount corresponding to the element with the lowest risk value in the high risk interval is acquired as the high risk threshold, and the historical precipitation amount corresponding to the element with the lowest risk value in the extremely high risk interval is acquired as the extremely high risk threshold.
Specifically, the low risk threshold, the medium risk threshold, the high risk threshold and the high risk threshold are early warning risk norms, and the risk degree of each side slope can be judged based on the precipitation amount so as to obtain a prediction result of the side slope instability.
Specifically, because the artificial side slope and the natural side slope have different difference thresholds, the artificial side slope and the natural side slope can be respectively calculated in the scheme, so that a low risk threshold, a medium risk threshold, a high risk threshold and an extremely high risk threshold which are suitable for the artificial side slope and a low risk threshold, a medium risk threshold, a high risk threshold and an extremely high risk threshold which are suitable for the natural side slope are obtained.
Specifically, the slope image of different time nodes is compared and analyzed to quickly and accurately obtain the risk information of slope instability, and compared with the traditional geological survey method, the method not only improves the accuracy and efficiency of prediction, but also reduces the labor cost and the time cost.
in the "acquire future precipitation of the side slope to be calculated" step, the future 42-hour precipitation of the side slope to be calculated is acquired.
In the step of carrying out matching analysis on the future precipitation of the side slope to be calculated and the low risk threshold, the middle risk threshold, the high risk threshold and the extremely high risk threshold to obtain a side slope instability prediction result of the side slope to be calculated, if the future precipitation of the side slope to be calculated is smaller than the low risk threshold, the prediction result of the side slope to be calculated is no instability risk, if the future precipitation of the side slope to be calculated is larger than the low risk threshold and smaller than the middle risk threshold, the prediction result of the side slope to be calculated is a first-level instability risk, if the future precipitation of the side slope to be calculated is larger than the middle risk threshold and smaller than the high risk threshold, the prediction result of the side slope to be calculated is a second-level instability risk, if the future precipitation of the side slope to be calculated is larger than the high risk threshold and smaller than the extremely high risk threshold, the prediction result of the side slope to be calculated is a third-level instability risk.
specifically, the scheme updates the future precipitation in real time to predict the slope instability of the slope to be calculated.
Specifically, when the slope types are divided into artificial slopes and natural slopes, the slope instability prediction is performed according to the type of the slope to be calculated.
Specifically, the scheme visually displays the slope instability prediction result of each slope in the map.
Specifically, the scheme can utilize the system tool to automatically run, generate early warning information in real time, provide powerful support for geological disaster prevention and control, reduce the influence of human intervention and improve the objectivity and accuracy of prediction.
according to the scheme, early warning information is generated in real time, and the method can timely and effectively cope with geological disasters. The early warning information can be timely transmitted to related departments and personnel so as to take effective control measures and reduce the loss caused by disasters.
Example two
Based on the same conception, referring to fig. 3, the application also provides a slope instability prediction device based on a map image analysis technology, comprising:
the acquisition module is used for acquiring historical map data of the region to be predicted and labeling each side slope position in the historical map data;
The difference calculation module is used for acquiring slope images of each slope position at different time nodes in the historical map data, calculating the difference degree of the slope images of the same slope position at two adjacent time nodes, and taking the slope image with the time node behind as a non-safety slope image when the difference degree of the slope is larger than a difference threshold value;
The threshold value calculation module is used for calculating the gradient image difference degree of each unsafe gradient image as a risk value of the corresponding unsafe gradient image, performing descending order sequencing on the risk values of all the unsafe gradient images to obtain a risk sequence, acquiring the historical precipitation corresponding to each element in the risk sequence, dividing the risk sequence to obtain a low risk interval, a medium risk interval, a high risk interval and an extremely high risk interval, and acquiring a low risk threshold value, a medium risk threshold value, a high risk threshold value and an extremely high risk threshold value based on each element in the low risk interval, the medium risk interval, the high risk interval and the extremely high risk interval and the corresponding historical precipitation;
The prediction module is used for obtaining the future precipitation of the slope to be calculated, and performing matching analysis on the future precipitation of the slope to be calculated and the low risk threshold, the medium risk threshold, the high risk threshold and the extremely high risk threshold to obtain a slope instability prediction result of the slope to be calculated.
Example III
This embodiment also provides an electronic device, referring to fig. 4, comprising a memory 404 and a processor 402, the memory 404 having stored therein a computer program, the processor 402 being arranged to run the computer program to perform the steps of any of the method embodiments described above.
In particular, the processor 402 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured as one or more integrated circuits that implement embodiments of the present application.
The memory 404 may include, among other things, mass storage 404 for data or instructions. By way of example, and not limitation, memory 404 may comprise a Hard Disk Drive (HDD), floppy disk drive, solid State Drive (SSD), flash memory, optical disk, magneto-optical disk, tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Memory 404 may include removable or non-removable (or fixed) media, where appropriate. Memory 404 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 404 is a Non-Volatile (Non-Volatile) memory. In particular embodiments, memory 404 includes Read-only memory (ROM) and Random Access Memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), an electrically rewritable ROM (EAROM) or FLASH memory (FLASH) or a combination of two or more of these. The RAM may be Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM) where appropriate, and the DRAM may be fast page mode dynamic random access memory 404 (FPMDRAM), extended Data Output Dynamic Random Access Memory (EDODRAM), synchronous Dynamic Random Access Memory (SDRAM), or the like.
memory 404 may be used to store or cache various data files that need to be processed and/or used for communication, as well as possible computer program instructions for execution by processor 402.
the processor 402 reads and executes the computer program instructions stored in the memory 404 to implement any of the slope instability prediction methods based on the map image analysis technology in the above embodiments.
optionally, the electronic apparatus may further include a transmission device 406 and an input/output device 408, where the transmission device 406 is connected to the processor 402 and the input/output device 408 is connected to the processor 402.
The transmission device 406 may be used to receive or transmit data via a network. Specific examples of the network described above may include a wired or wireless network provided by a communication provider of the electronic device. In one example, the transmission device includes a network adapter (Network Interface Controller, simply referred to as NIC) that can connect to other network devices through the base station to communicate with the internet. In one example, the transmission device 406 may be a Radio Frequency (RF) module, which is configured to communicate with the internet wirelessly.
The input-output device 408 is used to input or output information. In this embodiment, the input information may be historical map data, future precipitation, etc., and the output information may be a slope instability prediction result, etc.
alternatively, in the present embodiment, the above-mentioned processor 402 may be configured to execute the following steps by a computer program:
S101, acquiring historical map data of a region to be predicted and marking the position of each side slope in the historical map data;
S102, acquiring slope images of each slope position at different time nodes in historical map data, calculating the difference degree of the slope images of the same slope position at two adjacent time nodes, and taking the slope image with the rear time node as a non-safety slope image when the difference degree of the slope is larger than a difference threshold value;
S103, calculating the gradient image difference degree of each unsafe gradient image as a risk value of the corresponding unsafe gradient image, ordering the risk values of all unsafe gradient images in a descending order to obtain a risk sequence, obtaining the historical precipitation corresponding to each element in the risk sequence, dividing the risk sequence to obtain a low risk interval, a medium risk interval, a high risk interval and an extremely high risk interval, and obtaining a low risk threshold, a medium risk threshold, a high risk threshold and an extremely high risk threshold based on each element in the low risk interval, the medium risk interval, the high risk interval and the extremely high risk interval and the corresponding historical precipitation;
S104, acquiring the future precipitation of the side slope to be calculated, and carrying out matching analysis on the future precipitation of the side slope to be calculated and the low risk threshold, the medium risk threshold, the high risk threshold and the extremely high risk threshold to obtain a side slope instability prediction result of the side slope to be calculated.
It should be noted that, specific examples in this embodiment may refer to examples described in the foregoing embodiments and alternative implementations, and this embodiment is not repeated herein.
In general, the various embodiments may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although the invention is not limited thereto. While various aspects of the invention may be illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that these blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.
Embodiments of the invention may be implemented by computer software executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets, and/or macros can be stored in any apparatus-readable data storage medium and they include program instructions for performing particular tasks. The computer program product may include one or more computer-executable components configured to perform embodiments when the program is run. The one or more computer-executable components may be at least one software code or a portion thereof. In this regard, it should also be noted that any block of the logic flow as in fig. 4 may represent a procedure step, or interconnected logic circuits, blocks and functions, or a combination of procedure steps and logic circuits, blocks and functions. The software may be stored on a physical medium such as a memory chip or memory block implemented within a processor, a magnetic medium such as a hard disk or floppy disk, and an optical medium such as, for example, a DVD and its data variants, a CD, etc. The physical medium is a non-transitory medium.
It should be understood by those skilled in the art that the technical features of the above embodiments may be combined in any manner, and for brevity, all of the possible combinations of the technical features of the above embodiments are not described, however, they should be considered as being within the scope of the description provided herein, as long as there is no contradiction between the combinations of the technical features.
The foregoing examples illustrate only a few embodiments of the application, which are described in greater detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. A slope instability prediction method based on a map image analysis technology is characterized by comprising the following steps:
acquiring historical map data of a region to be predicted and marking the position of each side slope in the historical map data;
Acquiring slope images of each slope position at different time nodes in historical map data, calculating the difference degree of the slope images of the same slope position at two adjacent time nodes, and taking the slope image with the rear time node as a non-safety slope image when the difference degree of the slope is larger than a difference threshold value;
The method comprises the steps of taking the gradient image difference degree of each non-safety gradient image as a risk value of a corresponding non-safety gradient image, ordering the risk values of all the non-safety gradient images in a descending order to obtain a risk sequence, obtaining historical precipitation corresponding to each element in the risk sequence, dividing the risk sequence to obtain a low risk interval, a medium risk interval, a high risk interval and an extremely high risk interval, and obtaining a low risk threshold, a medium risk threshold, a high risk threshold and an extremely high risk threshold based on each element in the low risk interval, the medium risk interval, the high risk interval and the extremely high risk interval and the corresponding historical precipitation;
And obtaining the future precipitation of the side slope to be calculated, and carrying out matching analysis on the future precipitation of the side slope to be calculated and the low risk threshold, the medium risk threshold, the high risk threshold and the extremely high risk threshold to obtain a side slope instability prediction result of the side slope to be calculated.
2. The slope instability prediction method based on map image analysis technology according to claim 1, wherein in the step of acquiring historical map data of a region to be predicted and labeling each slope position in the historical map data, the historical map data is subjected to feature extraction and then is input into a pre-trained slope detection model to acquire and label the slope position.
3. The slope instability prediction method based on map image analysis technology according to claim 1, wherein the structural similarity index is used to determine the slope image difference degree of the same slope position at two adjacent time nodes.
4. The slope instability prediction method based on map image analysis technology according to claim 1, wherein in the step of acquiring the historical precipitation amount corresponding to each element in the risk sequence, the corresponding historical precipitation amount is acquired based on the shooting time and the GIS position of each element in the risk sequence.
5. the slope instability prediction method based on map image analysis technology according to claim 1, wherein in the step of dividing the risk sequence to obtain a low risk interval, a medium risk interval, a high risk interval and an extremely high risk interval, the risk sequence is divided into a front end, a middle end, a rear end and an end according to a proportion, wherein the front end is the low risk interval, the middle end is the medium risk interval, the rear end is the high risk interval, and the end is the extremely high risk interval.
6. The slope destabilizing prediction method based on map image analysis technique according to claim 1, wherein in the step of acquiring a low risk threshold, a medium risk threshold, a high risk threshold, and a high risk threshold based on each element in a low risk interval, a medium risk interval, a high risk interval, and a high risk interval and corresponding historical precipitation amounts, historical precipitation amounts corresponding to elements with lowest risk values in the low risk interval are acquired as the low risk threshold, historical precipitation amounts corresponding to elements with lowest risk values in the medium risk interval are acquired as the medium risk threshold, historical precipitation amounts corresponding to elements with lowest risk values in the high risk interval are acquired as the high risk threshold, and historical precipitation amounts corresponding to elements with lowest risk values in the high risk interval are acquired as the high risk threshold.
7. The slope instability prediction method based on map image analysis technology according to claim 1, wherein in the step of obtaining a slope instability prediction result of a slope to be calculated by performing matching analysis on a future precipitation of the slope to be calculated and a low risk threshold, a middle risk threshold, a high risk threshold and an extremely high risk threshold, if the future precipitation of the slope to be calculated is smaller than the low risk threshold, the prediction result of the slope to be calculated is no instability risk, if the future precipitation of the slope to be calculated is larger than the low risk threshold and smaller than the middle risk threshold, the prediction result of the slope to be calculated is a primary instability risk, if the future precipitation of the slope to be calculated is larger than the middle risk threshold and smaller than the high risk threshold, the prediction result of the slope to be calculated is a secondary instability risk, and if the future precipitation of the slope to be calculated is larger than the high risk threshold and smaller than the extremely high risk threshold, the prediction result of the slope to be calculated is a tertiary instability risk, and if the future precipitation of the slope to be calculated is larger than the high risk threshold.
8. slope instability prediction device based on map image analysis technique, characterized by comprising:
the acquisition module is used for acquiring historical map data of the region to be predicted and labeling each side slope position in the historical map data;
The difference calculation module is used for acquiring slope images of each slope position at different time nodes in the historical map data, calculating the difference degree of the slope images of the same slope position at two adjacent time nodes, and taking the slope image with the time node behind as a non-safety slope image when the difference degree of the slope is larger than a difference threshold value;
The threshold value calculation module is used for calculating the gradient image difference degree of each unsafe gradient image as a risk value of the corresponding unsafe gradient image, performing descending order sequencing on the risk values of all the unsafe gradient images to obtain a risk sequence, acquiring the historical precipitation corresponding to each element in the risk sequence, dividing the risk sequence to obtain a low risk interval, a medium risk interval, a high risk interval and an extremely high risk interval, and acquiring a low risk threshold value, a medium risk threshold value, a high risk threshold value and an extremely high risk threshold value based on each element in the low risk interval, the medium risk interval, the high risk interval and the extremely high risk interval and the corresponding historical precipitation;
The prediction module is used for obtaining the future precipitation of the slope to be calculated, and performing matching analysis on the future precipitation of the slope to be calculated and the low risk threshold, the medium risk threshold, the high risk threshold and the extremely high risk threshold to obtain a slope instability prediction result of the slope to be calculated.
9. an electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, the processor being arranged to run the computer program to perform a slope instability prediction method based on map image analysis techniques as claimed in any of claims 1-7.
10. A readable storage medium, wherein a computer program is stored in the readable storage medium, the computer program comprising program code for controlling a process to execute the process, the process comprising a slope instability prediction method based on map image analysis technology according to any one of claims 1 to 7.
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| US20170268874A1 (en) * | 2014-08-21 | 2017-09-21 | Nec Corporation | Slope monitoring system, device for slope stability analysis, method, and program |
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| CN117037421A (en) * | 2023-06-15 | 2023-11-10 | 四川省地质矿产勘查开发局区域地质调查队 | Rain-falling landslide hidden danger group meteorological risk early warning method, equipment and storage medium |
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| US20170268874A1 (en) * | 2014-08-21 | 2017-09-21 | Nec Corporation | Slope monitoring system, device for slope stability analysis, method, and program |
| CN116343436A (en) * | 2022-12-28 | 2023-06-27 | 浙江大华技术股份有限公司 | Landslide detection method, landslide detection device, landslide detection equipment and landslide detection medium |
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