Prediction method, device and equipment for water content of side slope and medium

Abstract

The invention provides a side slope water content prediction method, device and equipment and a medium, the method comprises the following steps: obtaining position information of a target side slope and water content detection data in a past time period, the past time period comprising a time period from N days before the current time to the current time; based on the position information of the target slope, determining grid meteorological data of at least one key meteorological grid corresponding to the target slope in a target time period, the target time period including a past time period and a prediction time period; the precipitation amount in the grid meteorological data is extracted, and the precipitation amount of the target slope in the target time period is obtained; and predicting the moisture content data of the target slope in the prediction time period based on the precipitation amount in the target time period and the moisture content detection data in the past time period by using the moisture content prediction model of the target slope. According to the method, accurate prediction of the water content of the side slope can be realized, and the time demand of beforehand treatment of side slope water damage is met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water content prediction, and in particular to a slope water content prediction method, device, equipment and medium. BACKGROUND

[0002] The slope is a sloping structure with a certain slope constructed on both sides of the subgrade in the subgrade engineering to ensure the stability of the subgrade. Its core function is to disperse soil pressure and guide surface runoff through reasonable slope design, thereby maintaining the stability of the subgrade and the surrounding terrain. As the intersection of geological environment and artificial engineering, the stability of the slope is directly related to the safety of the ecological environment, the integrity of the infrastructure, and the safety of the public's life and property. Once it loses stability, it may trigger a chain disaster effect.

[0003] The slope water content (also known as slope water content) refers to the amount of water contained in the soil of the slope, which is usually expressed in the form of percentage of weight or volume. Different rainfall intensities and time sequences cause changes in the distribution of slope water content. And the rise of slope water content directly causes phenomena such as water penetration saturation and progressive destruction of rock-soil mass, thereby causing slope water damage. Therefore, by detecting the slope water content, it can indirectly predict whether the slope water damage occurs.

[0004] However, the monitoring time window of the existing slope water content detection scheme (such as the sensor detection scheme) is very short, which cannot meet the time requirement of slope water damage pre-treatment. SUMMARY

[0005] Therefore, the present application provides a slope water content prediction method, device, equipment and medium, which can predict the slope water content in the prediction time by the precipitation in the past time and the prediction time and the water content detection data in the past time, thereby realizing accurate prediction of the slope water content and meeting the time requirement of slope water damage pre-treatment.

[0006] In a first aspect, a slope water content prediction method is provided, comprising: obtaining position information of a target slope and water content detection data in a past time period, the past time period including a time period from N days before the current time to the current time; determining grid meteorological data of at least one key meteorological grid corresponding to the target slope in a target time period based on the position information of the target slope, the target time period including the past time period and a prediction time period; extracting the precipitation in the grid meteorological data to obtain the precipitation of the target slope in the target time period; and using a water content prediction model of the target slope, predicting the water content data of the target slope in the prediction time period based on the precipitation in the target time period and the water content detection data in the past time period.

[0007] In a second aspect, a device for predicting a water content of a slope is provided, and the device comprises: an acquisition module configured to acquire position information of a target slope and water content detection data of a past time period, the past time period including a time period from N days before a current time to the current time; a determination module configured to determine, based on the position information of the target slope, grid meteorological data of at least one key meteorological grid corresponding to the target slope in a target time period, the target time period including the past time period and a prediction time period; an extraction module configured to extract precipitation in the grid meteorological data to obtain precipitation of the target slope in the target time period; and a prediction module configured to predict, based on the precipitation in the target time period and the water content detection data of the past time period, water content data of the target slope in the prediction time period by using a water content prediction model of the target slope.

[0008] In a third aspect, an electronic device is provided, which comprises a processor, a memory, and a program stored in the memory and capable of running on the processor, and when the program is executed by the processor, the steps of the method for predicting a water content of a slope according to any one of the embodiments of the present application are implemented.

[0009] In a fourth aspect, a computer-readable storage medium is provided, and the computer-readable storage medium stores instructions, and when the instructions are executed by a processor, the steps of the method for predicting a water content of a slope according to any one of the embodiments of the present application are implemented.

[0010] In summary, the method, device, equipment, and medium for predicting a water content of a slope provided in the present application have the following beneficial effects: by acquiring position information of a target slope and water content detection data of a past time period, the spatial position of the target slope can be accurately located, and the long-term dynamic change rule of the water content of the slope can be provided for a water content prediction model. Moreover, based on the position information of the target slope, grid meteorological data of at least one key meteorological grid corresponding to the target slope in a target time period is determined, and precipitation in the target time period is extracted, so that the key external factors affecting the change of the water content of the slope can be quantified, and more comprehensive environmental variables can be injected into the prediction model. In addition, by combining the water content detection data of the past time period and the grid meteorological data of the target time period, and by using an advanced water content prediction model, the change of the water content of the slope in a prediction time period can be accurately predicted without the attribute parameters of the target slope. In this way, by fusing real monitoring data and environmental variables, the internal mechanism and external driving factors affecting the change of the water content of the slope are comprehensively captured, the accuracy and reliability of the prediction result are significantly improved, strong data support is provided for slope stability evaluation and slope water damage disaster warning, and the time requirement of slope water damage prevention is met. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0012] Figure 1 A flowchart of a method for predicting the water content of a slope according to an embodiment of the present application is shown.

[0013] Figure 2 And Figure 3 A schematic diagram of the positional relationship between a target slope and a weather grid according to an embodiment of the present application is shown.

[0014] Figure 4 A structural schematic diagram of a device for predicting the water content of a slope according to an embodiment of the present application is shown.

[0015] Figure 5 A structural schematic diagram of an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0016] In order to make the above and other features and advantages of the present application clearer, the present application will be further described below with reference to the drawings. It should be understood that the specific embodiments given herein are for the purpose of explanation and are only exemplary, but are not limiting.

[0017] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. It will be apparent, however, to one skilled in the art that the present application can be practiced without the specific details, as not all specific details are necessary to practice the present application. In other instances, well-known steps or operations are not described in detail in order to avoid obscuring the present application.

[0018] The present inventors have found that the change in water content often lags behind the rainfall event, and needs to be associated with long-period rainfall data for several days or even weeks for accurate correlation analysis to reveal the evolution law of the water content of the specified slope. Therefore, the technical solutions of the present application are proposed, and the rainfall amount of the target slope in the target time period can be accurately obtained by covering the key weather grid of the target slope, and the rainfall amount is used as an input parameter of the water content prediction model of the target slope, so that the water content data of the target slope in the prediction time period can be accurately predicted.

[0019] The present application provides a method for predicting the water content of a slope, which is applied to a device for predicting the water content of a slope. Figure 1 A flowchart of a method for predicting the water content of a slope according to an embodiment of the present application is shown. Figure 1As shown, the prediction method of the water content of the slope can include the following steps.

[0020] In step S11, the position information of the target slope and the water content detection data of the past time period are obtained.

[0021] The target slope involved in an embodiment of the present application can be the slope of any roadbed. The position information of the target slope can be geographic position information, which can include the name of a place, an address, or latitude and longitude coordinates.

[0022] The past time period involved in an embodiment of the present application includes a time period from N days before the current time to the current time. N can be determined according to the longest continuous rainfall duration of the target slope.

[0023] In an embodiment of the present application, N can be the result of rounding off the product of the longest continuous rainfall duration, a period multiple, and a prediction duration. The period multiple is not less than 1 and not greater than 2. The prediction duration can be the duration of the prediction time period.

[0024] For example, the longest continuous rainfall duration is 15 days, the period multiple is 1.5, the prediction duration is 1 day, and N is the result of rounding off 15 x 1.5 - 1, that is, 22.

[0025] The water content detection data involved in an embodiment of the present application can be detected according to water content detection sensors arranged at different positions inside the target slope. The water content detection data of the past time period can include the water content detection value of each time step in the past time period. The water content detection sensor can be a humidity sensor.

[0026] In step S12, based on the position information of the target slope, the grid meteorological data of at least one key meteorological grid corresponding to the target slope in the target time period is determined.

[0027] The target time period involved in an embodiment of the present application includes the past time period and a prediction time period. The prediction time period can include a continuous time interval from the current time to M days after the current time. M days can be the duration of the prediction time period, that is, the prediction duration. In an embodiment of the present application, N is much greater than M, that is, N is at least 10 greater than M. Alternatively, M is 1.

[0028] In an embodiment of the present application, the duration of the target time period is determined according to the longest continuous rainfall duration of the at least one key meteorological grid. Alternatively, the duration of the target time period is the number of days obtained by rounding up the result of multiplying the longest continuous rainfall duration by a period multiple.

[0029] For example, the longest continuous rainfall duration is 15 days, the cycle multiple is 1.5, the target time period is 15×1.5 rounded up to 23 days, the past time period is 22 days, and the forecast time period is 1 day.

[0030] The key meteorological grid involved in an embodiment of the present application refers to a meteorological grid that can cover the target slope. The number of key meteorological grids is not less than one. Figure 2 and Figure 3 A schematic diagram showing the positional relationship between the target slope and the meteorological grid provided in one embodiment of the present application is shown in FIG. Figure 2 As shown in , the target slope is covered by a meteorological grid, which is the key meteorological grid. Figure 3 As shown, the target slope is covered by two meteorological grids (ie, meteorological grid A and meteorological grid B), which are key meteorological grids.

[0031] In one embodiment of the present application, a meteorological grid refers to a grid cell formed by spatially discretizing a geographic area. Grid meteorological data is a data set of meteorological element values ​​for all key meteorological grids during a target time period. Grid meteorological data includes at least temperature, precipitation, wind speed, and humidity. Grid meteorological data for at least one key meteorological grid during a target time period includes meteorological element values ​​for all key meteorological grids at each time step within the target time period.

[0032] The grid meteorological data in the target time period involved in one embodiment of the present application includes grid meteorological data in the past time period and grid meteorological data in the forecast time period. The grid meteorological data in the forecast time period is forecast weather data, and the grid meteorological data in the past time period is historical weather data.

[0033] In one embodiment of the present application, the target slope can be matched with a meteorological grid system based on its location information to determine all key meteorological grids covering the target slope. Grid meteorological data for all key meteorological grids during a target time period can then be obtained from a meteorological platform.

[0034] Step S13: extracting the precipitation in the grid meteorological data to obtain the precipitation of the target slope in the target time period.

[0035] The precipitation amount of the target slope in the target time period involved in one embodiment of the present application may include the precipitation amount of the target slope in each time step within the target time period, wherein the time step may be 1 hour.

[0036] In an embodiment of the present application, the precipitation of each key meteorological grid in the target time period can be screened out from the grid meteorological data, so that the precipitation of the target slope in the target time period can be obtained based on the precipitation of each key meteorological grid in the target time period.

[0037] In step S14, the water content prediction model of the target slope is used to predict the water content data of the target slope in the prediction time period based on the precipitation in the target time period and the water content detection data in the past time period.

[0038] The water content prediction model of the target slope in an embodiment of the present application is a prediction model corresponding to the target slope only. That is, one slope corresponds to one water content prediction model, and the parameters of the water content prediction models of different slopes are different. The water content prediction model can be a deep learning model or a machine learning model that can complete the prediction task.

[0039] In an embodiment of the present application, the water content prediction model is trained based on the historical water content data of the target slope and the historical precipitation, that is, the training set of the water content prediction model includes the historical water content detection data, the historical water content data and the historical precipitation of the target slope. In addition, the parameters of the water content prediction model are trained using the training set.

[0040] The historical precipitation in an embodiment of the present application can include the precipitation of the target slope in the historical time period, and can be obtained according to the historical grid meteorological data of the key meteorological grid of the target slope.

[0041] The historical water content data in an embodiment of the present application can include the water content of each position of the slope in the historical time period. The historical time period refers to any time period earlier than the current time, and the length of the historical time period is not less than the length of the target time period.

[0042] The historical water content detection data in an embodiment of the present application can include the data detected by the water content detection sensor arranged at different depths and positions of the target slope in the historical time period.

[0043] In an embodiment of the present application, the historical water content data can be obtained according to the historical water content detection data. For example, the historical water content data is obtained by using an interpolation method according to the historical water content detection data.

[0044] It should be noted that before training, the water content data of the historical time period, the water content detection data and the precipitation need to be processed into a plurality of training samples of different time periods, each training sample has a time length same as the time length of the target time period, which can be expressed as T. The training samples are divided into input data and label data, wherein the input data is the water content detection data of the previous (T-N) days and the T-day precipitation in the training sample, and the label data is the water content data of the last M days in the training sample. In the training process, the input data is input into the water content prediction model, the water content data of the target slope in the last M days is output, and the parameters of the water content prediction model are adjusted according to the difference between the water content data of the target slope in the last M days and the label data.

[0045] An embodiment of the present application relates to the water content data of the target slope in the prediction time period, which can include the water content of each position inside the target slope at each time step in the prediction time period. For example, the water content of each position inside the target slope at each hour in the prediction time period.

[0046] In an embodiment of the present application, the precipitation of the target time period and the water content detection data of the past time period are input into the water content prediction model as input parameters of the water content prediction model, and the water content data of the target slope in the prediction time period is output through the processing of the water content prediction model.

[0047] In some embodiments described above, by obtaining the position information of the target slope and the water content detection data of the past time period, the spatial position of the target slope can be accurately located, and the long-term dynamic change rule of the slope water content can be provided for the water content prediction model. Based on the position information of the target slope, the grid meteorological data of at least one key meteorological grid corresponding to the target slope in the target time period is determined, and the precipitation in the target time period is extracted, so that the key external factors affecting the change of the slope water content can be quantified, and more comprehensive environmental variables can be injected into the prediction model. In addition, by combining the water content detection data of the past time period and the grid meteorological data of the target time period, and using an advanced water content prediction model, the change of the slope water content in the prediction time period can be accurately predicted. In this way, by fusing real monitoring data and multi-source environmental variables, the internal mechanism and external driving factors affecting the change of the slope water content can be captured in all directions, the accuracy and reliability of the prediction results are significantly improved, and strong data support is provided for slope stability evaluation and slope water damage disaster warning applications, which meets the time requirements of slope water damage prevention.

[0048] In some embodiments, the step S12 of determining the grid meteorological data of the at least one key meteorological grid corresponding to the target slope in the target time period based on the position information of the target slope can include: determining a boundary position of the target slope based on the position information of the target slope; obtaining the at least one key meteorological grid corresponding to the target slope according to the boundary position; and obtaining the grid meteorological data of the at least one key meteorological grid in the target time period.

[0049] The boundary position involved in an embodiment of the present application can include the latitude and longitude coordinates of the boundary. In an embodiment of the present application, if the position information of the target slope is a place name or an address, the place name or the address can be converted into latitude and longitude coordinates, and the boundary position of the target slope can be accurately identified based on the latitude and longitude coordinates of the target slope by using a boundary identification algorithm.

[0050] In an embodiment of the present application, the boundary position of the target slope is matched with the meteorological grid system, one or more key meteorological grids covering the target slope are determined from the meteorological grid system, and the grid meteorological data is obtained from the meteorological platform according to the key meteorological grid and the time range of the target time period.

[0051] In some embodiments described above, the boundary position of the target slope is determined based on the position information of the target slope, so that the corresponding key meteorological grid and the grid meteorological data of the target time period can be accurately obtained, thereby realizing accurate matching of the meteorological grid data and the slope position, ensuring the spatio-temporal consistency of the meteorological information and the actual condition of the slope, reducing the prediction error caused by the mismatch of data, and providing a basis for the subsequent prediction of the water content of the slope.

[0052] In some embodiments, the at least one key meteorological grid corresponding to the target slope can be obtained according to the boundary position, which can include: determining the longest side length of the target slope according to the boundary position; selecting a spatial resolution of the key meteorological grid matched with the target slope according to the longest side length of the target slope; and obtaining the at least one key meteorological grid corresponding to the target slope based on the spatial resolution of the key meteorological grid.

[0053] In an embodiment of the present application, a polygon of the target slope is constructed according to the latitude and longitude coordinates of the boundary of the target slope, all sides of the polygon are traversed, the lengths of the side lengths are calculated, and the maximum value is found out from all the lengths of the side lengths, which is the longest side length.

[0054] The spatial resolution involved in an embodiment of the present application refers to the size of the actual ground area represented by each meteorological grid, which is usually in units of kilometers or meters. In an embodiment of the present application, the spatial resolution of the meteorological grid can include a plurality of hundred-meter-level resolutions and kilometer-level resolutions.

[0055] In an embodiment of the present application, selecting the spatial resolution of the key meteorological grid matched with the target slope refers to selecting a minimum spatial resolution that can accurately reflect the meteorological characteristics of the target slope, that is, selecting a spatial resolution that can meet the preset condition of the target slope to ensure sufficient spatial details.

[0056] The preset condition can be that the ratio of the longest side length to the selected spatial resolution is not more than a preset ratio. Optionally, the preset ratio can be 1 / 5.

[0057] For example, the longest side length of the target slope is 100 meters, and the spatial resolution of the key meteorological grid matched with the target slope can be 0.3 km x 0.3 km.

[0058] In an embodiment of the present application, after selecting the spatial resolution of the key meteorological grid, the meteorological grid system with the spatial resolution is matched with the target slope to obtain at least one key meteorological grid.

[0059] In some embodiments described above, the longest side length of the target slope is determined according to the boundary position of the target slope, and the spatial resolution that can retain sufficient spatial details of the target slope is selected to determine the key meteorological grid matched with the target slope.

[0060] In some embodiments, the step S13 of extracting the precipitation in the grid meteorological data to obtain the precipitation of the target slope in the target time period includes: when the number of the key meteorological grids is one, extracting the precipitation in the target time period in the grid meteorological data, and taking the precipitation as the precipitation of the target slope in the target time period.

[0061] In some embodiments, the step S13 of extracting the precipitation in the grid meteorological data to obtain the precipitation of the target slope in the target time period includes: when the number of the key meteorological grids is not less than two, determining the grid area of the target slope in each key meteorological grid and the corresponding grid weight coefficient; extracting the precipitation of each key meteorological grid in the target time period from the grid meteorological data; and obtaining the precipitation of the target slope in the target time period based on the grid area, the grid weight coefficient, and the precipitation of each key meteorological grid in the target time period.

[0062] The grid area involved in an embodiment of the present application can refer to the area covered by the key meteorological grid on the target slope. The grid weight coefficient can be the ratio of the grid area to the sum of all grid areas. The weight coefficient can reflect the contribution of each key grid to the precipitation of the target slope.

[0063] In an embodiment of the present application, the area of the intersection region of each key meteorological grid and the target slope is calculated, and the grid area of the target slope in each key meteorological grid is obtained. The ratio of each grid area to the sum of all grid areas is calculated, and the grid weight coefficient of each key meteorological grid is obtained. The precipitation in each key meteorological grid is multiplied by the corresponding grid weight coefficient, and the weighted contribution of the key meteorological grid to the target slope precipitation is obtained. The weighted contributions of all key meteorological grids are added, and the total precipitation of the target slope in the target time period is obtained.

[0064] In the above embodiment, by considering the relative contribution of different key meteorological grids to the target slope precipitation, a more accurate and reliable precipitation in the target time period can be obtained.

[0065] In some embodiments, after the water content rate of the target slope in the prediction time period is predicted based on the precipitation in the target time period and the water content rate detection data in the past time period in step S14, the slope water content rate prediction method further comprises: determining the rendering color of the three-dimensional geological model of the target slope according to the corresponding relationship between the water content rate and the rendering color and the water content rate data of the target slope.

[0066] The three-dimensional geological model of the target slope involved in an embodiment of the present application can reflect the geological characteristics and the three-dimensional structure of the target slope. The rendering color refers to the color of the rendering of each point of the three-dimensional geological model in the visualization interface. The corresponding relationship between the water content rate and the rendering color refers to the mapping rule between the water content rate and the rendering color.

[0067] In an embodiment of the present application, different water content rates can be divided into different water content intensities according to the meteorological water content rate intensity classification, and different water content intensities can correspond to different rendering colors. That is, the water content rate of each point of the target slope at each time step in the prediction time period is obtained, and the rendering color of each point of the three-dimensional geological model of the target slope at each time step in the prediction time period is determined according to the corresponding relationship between the water content rate and the rendering color.

[0068] For example, when the water content rate of a certain position of the target slope at a certain time in the prediction time period exceeds 60, it indicates a high-intensity water content rate, the high-intensity water content rate corresponds to red, the rendering color of the coordinate point corresponding to the position of the three-dimensional geological model is determined as red, and the coordinate point is rendered as red on the visualization interface.

[0069] It should be noted that one time step corresponds to one three-dimensional geological model of the target slope to be rendered.

[0070] In some of the above embodiments, the rendering color of the target slope three-dimensional model is determined according to the water content data, so that the three-dimensional geological model of the target slope on the visualization interface can be rendered in different colors, directly presenting the water content distribution rule, and facilitating the user to quickly find the dangerous water content area.

[0071] In another aspect, the present application provides a slope water content prediction device, Figure 4 The structure of the slope water content prediction device provided by an embodiment of the present application is shown in the structure diagram. Figure 4 As shown, the slope water content prediction device 40 can include the following modules.

[0072] The acquisition module 41 is configured to acquire the location information of the target slope and the water content detection data in the past time period, wherein the past time period includes a time period from N days before the current time to the current time.

[0073] The determination module 42 is configured to determine the grid meteorological data of at least one key meteorological grid corresponding to the target slope in the target time period based on the location information of the target slope, wherein the target time period includes the past time period and a prediction time period.

[0074] The extraction module 43 is configured to extract the precipitation in the grid meteorological data to obtain the precipitation of the target slope in the target time period.

[0075] The prediction module 44 is configured to predict the water content data of the target slope in the prediction time period by using a water content prediction model of the target slope based on the precipitation in the target time period and the water content detection data in the past time period.

[0076] In the above embodiment, by acquiring the location information of the target slope and the water content detection data in the past time period, the spatial position of the target slope can be accurately located and the long-term dynamic change rule of the slope water content can be provided for the water content prediction model. Moreover, based on the location information of the target slope, the grid meteorological data of at least one key meteorological grid corresponding to the target slope in the target time period is determined, and the precipitation in the target time period is extracted, so that the key external factors affecting the change of the slope water content can be quantified, and more comprehensive environmental variables are injected into the prediction model. In addition, by combining the water content detection data in the past time period and the grid meteorological data in the target time period, and using an advanced water content prediction model, the change of the slope water content in the prediction time period can be accurately predicted. In this way, by fusing real monitoring data and multi-source environmental variables, the internal mechanism and external driving factors affecting the change of the slope water content are captured in all directions, the accuracy and reliability of the prediction result are significantly improved, and strong data support is provided for slope stability evaluation and slope water damage disaster warning applications, meeting the time requirements of slope water damage prevention.

[0077] In some embodiments, the determining module 42 is specifically configured to determine a boundary position of the target slope based on the position information of the target slope; obtain at least one key meteorological grid corresponding to the target slope according to the boundary position; and acquire grid meteorological data of the at least one key meteorological grid in a target time period.

[0078] In some embodiments, the determining module 42 is further specifically configured to determine a longest side length of the target slope according to the boundary position; select a spatial resolution of a key meteorological grid matched with the target slope according to the longest side length of the target slope; and obtain the at least one key meteorological grid corresponding to the target slope based on the spatial resolution of the key meteorological grid.

[0079] In some embodiments, the extracting module 43 is specifically configured to, when the number of the key meteorological grids is not less than two, determine a grid area of the target slope in each key meteorological grid and a corresponding grid weight coefficient; extract a precipitation amount of each key meteorological grid in the target time period from the grid meteorological data; and obtain a precipitation amount of the target slope in the target time period based on the grid area, the grid weight coefficient, and the precipitation amount of each key meteorological grid in the target time period.

[0080] In some embodiments, the slope water content prediction apparatus 40 can further include a color determining module configured to determine a rendering color of the three-dimensional geological model of the target slope according to a corresponding relationship between the water content and the rendering color and water content data of the target slope.

[0081] It should be understood that the specific features, operations and details described above with respect to the method of the present application can be similarly applied to the device and system of the present application, or vice versa. In addition, each step of the method of the present application described above can be performed by the corresponding components or units of the device or system of the present application.

[0082] It should be understood that each module / unit of the device of the present application can be implemented in whole or in part by software, hardware, firmware, or a combination thereof. Each module / unit can be embedded in a processor of an electronic device in hardware or firmware form, or independent of the processor, or stored in a memory of the electronic device in software form to be invoked by the processor to perform the operations of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0083] In yet another aspect of the present application, an electronic device is provided. Figure 5 FIG. 1 shows a structural schematic diagram of an electronic device according to an embodiment of the present application, and Figure 5As shown, the electronic device 50 includes a processor 51, a memory 52, and a program stored in the memory and capable of running on the processor. When the program is executed by the processor, the steps of the slope moisture content prediction method provided in any of the above embodiments are implemented.

[0084] In one embodiment, the electronic device 50 may include a processor, memory, network interface, communication interface, etc. connected via a system bus. The processor of the electronic device 50 may be used to provide necessary computing, processing, and / or control capabilities. The memory of the electronic device 50 may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system, computer programs, etc. The internal memory may provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the electronic device 50 may be used to connect to and communicate with external devices via a network.

[0085] On the other hand, the present application provides a computer-readable storage medium storing instructions, wherein when the instructions are executed by a processor, the steps of the slope moisture content prediction method provided in any of the above embodiments are implemented.

[0086] Those skilled in the art will appreciate that the method steps of the present application can be performed by instructing relevant hardware such as electronic devices or processors through a computer program, and the computer program can be stored in a non-transitory computer-readable storage medium, which causes the steps of the present application to be performed when the computer program is executed. Depending on the circumstances, any reference to memory, storage or other media herein may include non-volatile or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0087] The various technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification as long as such combination does not conflict.

[0088] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting slope moisture content, characterized in that: include: Acquire the location information of the target slope and the water content detection data of the past time period, wherein the past time period includes the time period from N days before the current moment to the current moment; Based on the location information of the target slope, determining grid meteorological data of at least one key meteorological grid corresponding to the target slope in a target time period, the target time period including a past time period and a predicted time period; Extracting precipitation from the grid meteorological data to obtain precipitation on the target slope in a target time period; The moisture content prediction model of the target slope is used to predict the moisture content data of the target slope in the prediction time period based on the precipitation in the target time period and the moisture content detection data of the past time period.

2. The method for predicting slope moisture content according to claim 1, characterized in that: The determining, based on the location information of the target slope, grid meteorological data of at least one key meteorological grid corresponding to the target slope in a target time period includes: Determining a boundary position of the target slope based on the position information of the target slope; obtaining, according to the boundary position, at least one key meteorological grid corresponding to the target slope; Obtain grid meteorological data of the at least one key meteorological grid in a target time period.

3. The method for predicting slope moisture content according to claim 2, characterized in that: The step of obtaining at least one key meteorological grid corresponding to the target slope according to the boundary position includes: Determining the longest side length of the target slope according to the boundary position; Selecting a spatial resolution of a key meteorological grid that matches the target slope based on the longest side length of the target slope; At least one key meteorological grid corresponding to the target slope is obtained based on the spatial resolution of the key meteorological grid.

4. The method for predicting slope moisture content according to claim 1, characterized in that: The step of extracting the precipitation from the grid meteorological data to obtain the precipitation of the target slope in the target time period includes: When the number of the key meteorological grids is not less than two, determining the grid area of ​​the target slope in each key meteorological grid and the corresponding grid weight coefficient; Extracting the precipitation of each key meteorological grid in a target time period from the grid meteorological data; The precipitation of the target slope in the target time period is obtained based on the grid area, the grid weight coefficient and the precipitation of each key meteorological grid in the target time period.

5. The method for predicting slope moisture content according to claim 1, characterized in that: After predicting the moisture content data of the target slope within the prediction time period using the moisture content prediction model of the target slope based on the precipitation in the target time period and the moisture content detection data of the past time period, the method further includes: The rendering color of the three-dimensional geological model of the target slope is determined according to the corresponding relationship between the moisture content and the rendering color and the moisture content data of the target slope.

6. The method for predicting slope moisture content according to any one of claims 1 to 5, characterized in that: The duration of the target time period is determined according to the longest continuous rainfall duration of the at least one key meteorological grid.

7. The method for predicting slope moisture content according to any one of claims 1 to 5, characterized in that: The target slope moisture content prediction model is obtained by training based on historical moisture content detection data, historical moisture content data and historical precipitation of the target slope.

8. A device for predicting slope moisture content, characterized in that: include: An acquisition module is used to acquire the location information of the target slope and the water content detection data of the past time period, wherein the past time period includes the time period from N days before the current moment to the current moment; a determination module, configured to determine, based on the location information of the target slope, grid meteorological data of at least one key meteorological grid corresponding to the target slope in a target time period, wherein the target time period includes a past time period and a predicted time period; An extraction module, configured to extract precipitation from the grid meteorological data to obtain precipitation on the target slope in a target time period; The prediction module is used to use the moisture content prediction model of the target slope to predict the moisture content data of the target slope in the prediction time period based on the precipitation in the target time period and the moisture content detection data of the past time period.

9. An electronic device, characterized in that: The method comprises a processor, a memory and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the steps of the method for predicting the moisture content of a slope as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed by the processor, the steps of the slope moisture content prediction method according to any one of claims 1 to 7 are implemented.

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