High-density electrical method measuring system for monitoring landslides

Abstract

The application provides a high-density electrical method measurement system for mountain landslide monitoring, comprising: an acquisition unit arranged in a to-be-monitored area of a mountain slope, the acquisition unit being used for acquiring apparent resistivity of each monitoring point in the to-be-monitored area; a measurement terminal electrically connected with the acquisition unit; a communication unit wirelessly connected with the measurement terminal; a cloud server wirelessly connected with the communication unit; and a data processing unit of the cloud server, which is used for obtaining monitoring data of the to-be-monitored area after pre-processing the apparent resistivity. The system of the application can greatly reduce the labor input, effectively improve the work efficiency, and effectively improve the data processing efficiency through the wireless communication mode for data transmission, the cloud server for data collection and processing, and the intelligent data collection and processing.

Description

Technical Field

[0001] This invention relates to the field of landslide monitoring technology, and more specifically, to a high-density electrical resistivity tomography system for monitoring landslides. Background Technology

[0002] Currently, landslide monitoring uses the high-density resistivity method, which involves setting up field monitoring systems to monitor landslides in specific areas. However, existing methods require monitoring personnel to continuously collect data from the monitoring devices and then analyze the collected data, which increases manpower input and greatly reduces work efficiency. Summary of the Invention

[0003] In view of this, the present invention proposes a high-density electrical resistivity tomography system for landslide monitoring, aiming to solve the problem of how to improve work efficiency when monitoring landslides.

[0004] In one aspect, the present invention proposes a high-density electrical resistivity tomography system for landslide monitoring, comprising:

[0005] A data acquisition unit is set up in the area to be monitored on the mountain slope. The data acquisition unit is used to acquire the apparent resistivity of each monitoring point in the area to be monitored.

[0006] A measuring terminal, electrically connected to the acquisition unit, is used to receive the apparent resistivity acquired by the acquisition unit;

[0007] The communication unit is wirelessly connected to the measurement terminal;

[0008] A cloud server is wirelessly connected to the communication unit, and the measurement terminal transmits the received apparent resistivity to the cloud server through the communication unit; wherein,

[0009] The cloud server includes:

[0010] The data processing unit is used to preprocess the apparent resistivity to obtain monitoring data of the area to be monitored, obtain the apparent resistivity change rate of each monitoring point based on the monitoring data, obtain an apparent resistivity contour map based on the apparent resistivity change rate, and determine the sensitivity of the soil at each monitoring point to rainfall response based on the apparent resistivity contour map.

[0011] Furthermore, the data processing unit includes:

[0012] The monitoring data preprocessing module is used to preprocess the apparent resistivity after obtaining it to obtain gridded data, and to determine the monitoring data based on the gridded data.

[0013] Furthermore, the monitoring data preprocessing module includes:

[0014] The distortion point processing module is used to remove the distortion point data in the apparent resistivity and then obtain the first data.

[0015] The gridding processing module is used to perform gridding processing on the first data using the triangular mesh interpolation method, and then obtain the gridded data.

[0016] Furthermore, the data processing unit also includes:

[0017] The apparent resistivity change rate determination module is used to calculate the apparent resistivity change rate ρ at each of the monitoring points. δ The apparent resistivity change rate ρ δ Calculate according to the following formula:

[0018]

[0019] Where, ρ δ ρ is the rate of change of apparent resistivity, ρ0 is the reference value of apparent resistivity, and ρ i Let be the apparent resistivity value of the i-th measurement, and Δρ be the difference between the apparent resistivity value of the i-th measurement and the apparent resistivity reference value.

[0020] The time-series curve generation module is used to establish the apparent resistivity change rate ρ at each of the monitoring points. δ Create a time series graph;

[0021] The processing module is used to determine the information on the change of soil resistivity value with soil moisture content based on the slope of the time series curve.

[0022] Furthermore, the processing module is also used to calculate the slope k of the timing curve of the timing curve graph according to the following formula:

[0023] k = tanα

[0024] Where k is the slope of the time series curve, and α is the angle between the tangent line at a point in the time series curve graph and the x-axis.

[0025] Furthermore, the processing module is also configured to, when determining the information on the change of soil resistivity value with soil moisture content based on the slope of the time-series curve, include:

[0026] When α is an obtuse angle, k < 0, the slope of the time series curve is negative, and it can be judged that rainfall leads to an increase in soil moisture content and a decrease in apparent resistivity.

[0027] When α is an acute angle, k > 0, and the slope of the time series curve is positive. This indicates that drought is causing a decrease in soil moisture content and an increase in apparent resistivity.

[0028] Furthermore, the data processing unit also includes:

[0029] The contour profile generation module is used to generate an apparent resistivity contour profile based on the apparent resistivity change rate. Based on the apparent resistivity contour profile, it displays the distribution of apparent resistivity magnitude across the entire monitoring profile, as well as the electrical characteristics of low-resistivity and high-resistivity distributions.

[0030] The apparent resistivity contour map contains the distribution characteristics of the rate of change of apparent resistivity in the horizontal and vertical directions. The apparent resistivity contour maps are arranged in chronological order.

[0031] Furthermore, the contour section map establishment module is also used to establish the apparent resistivity change rate section map, including:

[0032] The absolute value of the difference between the maximum and minimum apparent resistivity change rate at each monitoring point within the monitoring period is selected as the maximum change value for plotting.

[0033] Furthermore, the data processing unit also includes:

[0034] The time-series curve generation module is used to generate a time-series curve of apparent resistivity change rate based on the apparent resistivity change rate of each monitoring point, so as to display different regions with varying degrees of apparent resistivity change rate.

[0035] Furthermore, when establishing the time-series curve graph, the time-series curve graph establishment module extracts specific and regular monitoring point data in the vertical and horizontal directions to draw the time-series curve graph of the apparent resistivity change rate.

[0036] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention acquires monitoring data of the area under monitoring by collecting the apparent resistivity of each monitoring point within the monitoring area, preprocessing the apparent resistivity, and obtaining the apparent resistivity change rate at each monitoring point based on the monitoring data. Based on the apparent resistivity change rate, an apparent resistivity contour map is obtained, and the sensitivity of the soil at each monitoring point to rainfall response is determined according to the apparent resistivity contour map. The system of this invention transmits data wirelessly and collects and processes data through a cloud server, thereby greatly reducing manpower input and effectively improving work efficiency. Furthermore, the intelligent data collection and processing method effectively improves data processing efficiency.

[0037] Furthermore, this invention can effectively reflect the trend of apparent resistivity of high and low resistivity bodies changing with soil moisture content fluctuations, thereby providing a comprehensive understanding of the geoelectric characteristics of apparent resistivity changing over time during the monitoring period. Attached Figure Description

[0038] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0039] Figure 1 This is a first functional block diagram of a high-density electrical resistivity tomography system for landslide monitoring provided in an embodiment of the present invention;

[0040] Figure 2 This is a second functional block diagram of a high-density electrical resistivity tomography (EDT) system for landslide monitoring provided in an embodiment of the present invention.

[0041] Figure 3 This is a third functional block diagram of a high-density electrical resistivity tomography (EDT) system for landslide monitoring provided in an embodiment of the present invention.

[0042] Figure 4 This is the original data point map provided in the embodiments of the present invention;

[0043] Figure 5 This is a data point map with distortion points removed, provided in an embodiment of the present invention.

[0044] Figure 6 This is a gridded data point map after triangulation provided in an embodiment of the present invention;

[0045] Figure 7 The slope in the timing curve provided in the embodiments of the present invention;

[0046] Figure 8 This is a schematic diagram showing the mapping data and contour lines provided in the embodiments of the present invention. Detailed Implementation

[0047] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0048] See Figure 1As shown in the figure, this embodiment provides a high-density electrical resistivity measurement system for landslide monitoring, including a data acquisition unit, a measurement terminal, a communication unit, and a cloud server. The data acquisition unit is set in the area to be monitored on the slope, and is used to collect the apparent resistivity of each monitoring point in the area. The measurement terminal is electrically connected to the data acquisition unit and is used to receive the apparent resistivity collected by the data acquisition unit. The communication unit is wirelessly connected to the measurement terminal. The cloud server is wirelessly connected to the communication unit, and the measurement terminal transmits the received apparent resistivity to the cloud server through the communication unit.

[0049] As is understood, apparent resistivity refers to the resistivity of a material or rock in underground electrical resistivity testing. It is the ratio of the distance between measuring electrodes to the depth of the measured medium. In this application, electrodes are arranged underground and current is passed through them. The resistivity is then calculated by measuring the potential difference between the electrodes. Apparent resistivity, on the other hand, is converted from resistivity to apparent resistivity by considering the ratio of the distance between the electrodes to the depth of the measured medium. Due to the complexity of the underground medium and the influence of factors such as electrode distance, apparent resistivity more accurately reflects the electrical properties of the soil, providing important evidence for landslide detection.

[0050] Combination Figure 2 As shown, specifically, the cloud server includes a data processing unit. The data processing unit is used to preprocess the apparent resistivity to obtain monitoring data of the area to be monitored, and to obtain the apparent resistivity change rate of each monitoring point based on the monitoring data. Based on the apparent resistivity change rate, an apparent resistivity contour map is obtained, and the sensitivity of the soil at each monitoring point to rainfall response is determined according to the apparent resistivity contour map.

[0051] The high-density remote monitoring system in this embodiment improves upon the high-density resistivity method by refining the field observation system. First, the existing ground-laid cables and electrodes are buried underground to ensure that each measurement is taken at the same location. The electrodes are connected to dedicated mounting brackets on the cables via cable leads and clamps. One end of the cable is connected to the connector of a multiplexer, and the signal output of the multiplexer is connected to the signal input of the control host. The RS232 serial communication interface of the control host is connected to the wireless transmission device, and the other end of the wireless transmission device is connected to a remote computer to transmit the measured data.

[0052] This embodiment enables long-distance wireless data transmission of the high-density electrical resistivity system via the Internet, employing remote control via a PC. Electrical resistivity measurements support real-time color block mapping, and upon completion, automatically generate contour maps, measurement point information, measurement progress, and pole running maps, providing a comprehensive view of the measurement information. Measurement data is automatically saved, and the generated files can be directly used for RES2DINV inversion mapping. The instrument's power supply can be remotely managed, eliminating the need for on-site personnel to turn the power on or off.

[0053] In the above embodiments, landslide monitoring data can be collected at a frequency of 1 to 3 times per day for a long-term monitoring period.

[0054] This invention collects apparent resistivity data from various monitoring points within a monitored area, preprocesses the apparent resistivity data to obtain monitoring data for the area, and obtains the apparent resistivity change rate at each monitoring point based on the monitoring data. Based on the apparent resistivity change rate, it generates an apparent resistivity contour map, and determines the soil sensitivity to rainfall at each monitoring point based on the apparent resistivity contour map. The system of this invention transmits data wirelessly and collects and processes data via a cloud server, thereby significantly reducing manpower and effectively improving work efficiency. Furthermore, the intelligent data collection and processing methods effectively enhance data processing efficiency.

[0055] See Figure 3 As shown, specifically, the data processing unit includes a monitoring data preprocessing module, which is used to preprocess the apparent resistivity after obtaining it to obtain gridded data, and to determine the monitoring data based on the gridded data.

[0056] Specifically, the monitoring data preprocessing module includes a distortion point processing module and a gridding processing module. The distortion point processing module is used to remove distortion point data in the apparent resistivity and obtain the first data. The gridding processing module is used to perform gridding processing on the first data using the triangular mesh interpolation method and obtain the gridded data.

[0057] Specifically, triangulation interpolation constructs a triangulation network using the coordinates and attribute values ​​of known points. A common method is the Delaunay triangulation algorithm. Then, the attribute values ​​of unknown points are calculated based on the triangles on the triangulation network. The preferred method is to calculate the attribute value of a point using the centroid interpolation method or the centroid coordinate interpolation method based on the coordinates and attribute values ​​of the three vertices of the triangle.

[0058] Combination Figure 4-6 As shown, the preprocessing of the apparent resistivity includes distortion point processing and meshing.

[0059] In actual high-density resistivity resistivity measurements, the electric field is affected by the interaction between electrodes and other uncertainties, often leading to discrepancies between the data and actual values, resulting in spurious resistance cross-sectional diagrams. If the grounding resistance of the grounding electrode is too high, it will directly affect the size of the current source circuit, further impacting the accuracy of the potential difference measurement, affecting the measurement cycle, and causing unstable or erroneous readings that interfere with the interpretation of anomalies. When measurement conditions cannot be improved, the only option is to record the data and then delete erroneous or distorted data points. In this study, the Swedish high-density processing software Res2dinv was used to process the apparent resistivity, directly eliminating distorted points.

[0060] The high-density electrical resistivity tomography (EPT) monitoring equipment uses a Winner device for continuous data acquisition. Due to its polarity, the location of data points changes at each layer. Additionally, there are missing measurement points due to the removal of distorted points. Therefore, the original data or the data after removing the distortion is processed by triangular interpolation to form a gridded data, which facilitates the acquisition of measurement point information at different depths in the vertical direction.

[0061] Specifically, the data processing unit further includes a module for determining the apparent resistivity change rate, a time-series curve creation module, and a processing module.

[0062] The apparent resistivity change rate determination module is used to calculate the apparent resistivity change rate ρ at each of the monitoring points. δ The apparent resistivity change rate ρ δ Calculate according to the following formula:

[0063]

[0064] Where, ρ δ ρ is the rate of change of apparent resistivity, ρ0 is the reference value of apparent resistivity, and ρ i Let be the apparent resistivity value of the i-th measurement, and Δρ be the difference between the apparent resistivity value of the i-th measurement and the apparent resistivity reference value.

[0065] Understandably, there are two ways to determine the apparent resistivity reference value ρ0. One is to avoid the rainy or dry season as much as possible, when the soil moisture content changes relatively steadily and the apparent resistivity changes relatively little. Therefore, when the rainy or dry season arrives, the apparent resistivity change rate will show positive and negative values ​​due to the fluctuation of soil moisture content and the distribution will be relatively uniform. The second is to choose a time as close as possible to the start and end time of monitoring.

[0066] The time-series curve generation module is used to establish the apparent resistivity change rate ρ at each of the monitoring points. δ Create a time series curve.

[0067] The processing module is used to determine the information on the change of soil resistivity value with soil moisture content based on the slope of the time series curve.

[0068] Combination Figure 7 As shown, specifically, the processing module is further configured to calculate the slope k of the time series curve according to the following formula:

[0069] k = tanα

[0070] Where k is the slope of the time series curve, and α is the angle between the tangent line at a point in the time series curve graph and the x-axis.

[0071] Specifically, the processing module is further configured to determine the information on the change of soil resistivity with soil moisture content based on the slope of the time-series curve of the time-series curve, including:

[0072] When α is an obtuse angle, k < 0, the slope of the time series curve is negative, and it can be judged that rainfall leads to an increase in soil moisture content and a decrease in apparent resistivity.

[0073] When α is an acute angle, k > 0, and the slope of the time series curve is positive. This indicates that drought is causing a decrease in soil moisture content and an increase in apparent resistivity.

[0074] In a time series graph, the larger the angle between the tangent line at a point and the x-axis, the greater the slope; conversely, the smaller the angle, the smaller the slope. When the angle is acute, k > 0, and the slope is positive; when the angle is obtuse, k < 0, and the slope is negative.

[0075] In the time-series curves, a negative slope for k < 0 indicates rainfall leading to increased soil moisture content and decreased apparent resistivity; a positive slope for k > 0 indicates drought leading to decreased soil moisture content and increased apparent resistivity. Neither the positive nor negative slope originates from zero; rather, it corresponds to the starting point of either rainfall or drought. Therefore, the slope of the curve better illustrates the consistency between the rate of change in apparent resistivity and the change in soil moisture content.

[0076] Specifically, the data processing unit further includes a time-series curve establishment module, which is used to establish a time-series curve of apparent resistivity change rate based on the apparent resistivity change rate of each monitoring point, so as to display different regions of varying strength of apparent resistivity change rate.

[0077] Specifically, when establishing the time-series curve graph, the time-series curve graph establishment module extracts specific and regular monitoring point data in the vertical and horizontal directions to draw the time-series curve graph of the apparent resistivity change rate.

[0078] In time-series curves, the main focus is on the positive and negative values ​​and the slope of the curve, as these reflect the changes in soil resistivity as soil moisture content changes. Negative values ​​in a time-series curve likely indicate that rainfall increases soil moisture content, leading to a decrease in apparent soil resistivity, while positive values ​​likely indicate that drought decreases soil moisture content, leading to an increase in apparent soil resistivity.

[0079] Specifically, the data processing unit further includes a contour map establishment module. This module is used to establish an apparent resistivity contour map based on the apparent resistivity change rate, and to display the apparent resistivity distribution of the entire monitoring profile based on the apparent resistivity contour map, as well as the electrical characteristics of low-resistivity and high-resistivity distributions.

[0080] The apparent resistivity contour map contains the distribution characteristics of the rate of change of apparent resistivity in the horizontal and vertical directions. The apparent resistivity contour maps are arranged in chronological order.

[0081] The contour section map establishment module is also used to establish the apparent resistivity change rate section map by selecting the absolute value of the difference between the maximum and minimum apparent resistivity change rates of each monitoring point within the monitoring period as its maximum change amount for map drawing.

[0082] In the above embodiments, based on the apparent resistivity or the rate of change calculated from the apparent resistivity, to make the data more intuitively reflect the changing pattern of soil resistivity under the influence of water content, the changing patterns contained in the data were extracted and explored from different aspects to more intuitively reflect the monitoring results. Two types of maps were mainly drawn: isopleth profiles of the apparent resistivity rate of change and time-series curves of the apparent resistivity rate of change. The isopleth profiles of the apparent resistivity rate of change can present the distribution of apparent resistivity magnitude across the entire monitoring profile, showing the electrical characteristics of low-resistivity and high-resistivity distributions; the time-series curves of the apparent resistivity rate of change can show different regions with varying degrees of apparent resistivity rate of change.

[0083] Apparent resistivity cross-sectional maps contain the distribution characteristics of the rate of change of apparent resistivity in both horizontal and vertical directions. By arranging the apparent resistivity cross-sectional maps in chronological order and comparing them with rainfall and soil moisture content curves, the cross-sectional maps can reflect the trend of apparent resistivity changes with soil moisture content fluctuations in high and low resistivity bodies, thus providing a comprehensive understanding of the geoelectric characteristics of apparent resistivity changes over time during the monitoring period.

[0084] The apparent resistivity change rate profile is plotted by taking the absolute value of the difference between the maximum and minimum change rate values ​​at each measuring point within the monitoring period as the maximum change amount. This can reflect the sensitivity of soil at different locations to rainfall response.

[0085] See Figure 8As shown, when constructing the time-series curve of apparent resistivity change rate, the high-density electrical resistivity monitoring data is distributed in an inverted triangle. Therefore, the number of measurement points in each column and row in the vertical and horizontal directions is different: in the horizontal direction, the number of measurement points decreases with increasing depth, with a maximum of 57 and a minimum of 3; in the vertical direction, the number of measurement points is highest on the central axis of the measurement line, gradually decreasing towards both ends, with a maximum of 19 and a minimum of 1. The gridded data is more regular, facilitating the extraction of data from different depths at the same point, and data from different points at the same depth. Compared to the apparent resistivity cross-sectional map, the time-series curve extracts specific and regular measurement point data in the vertical and horizontal directions, thus reflecting more local details and facilitating the extraction of more detailed variation characteristics from the apparent resistivity data. More details in the vertical and horizontal directions are used to analyze some characteristics of apparent resistivity changes.

[0086] In some embodiments of this application, the high-density electrical resistivity measurement system for landslide monitoring can also include an alarm unit. The alarm unit has a pre-set apparent resistivity safety threshold A. The alarm unit is electrically connected to the data processing unit and acquires data from the data processing unit. When one or more points in the detection data acquired by the data processing unit show real-time data below the apparent resistivity safety threshold A, the alarm unit issues an alert and judges the apparent resistivity change rate. When the apparent resistivity change rate remains negative for a period of time, the alarm unit issues an alarm and strengthens continuous monitoring of that point. The alarm unit can also analyze the apparent resistivity change phenomenon at that point based on its sensitivity to rainfall to identify and resolve system detection anomalies.

[0087] Understandably, the addition of an alert unit in this application enables timely response to data anomalies. The use of apparent resistivity and apparent resistivity change rate can effectively detect landslides. The detection results are verified based on the soil's sensitivity to rainfall, thereby improving the system's detection accuracy and effectively enhancing data reliability.

[0088] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0090] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0091] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A high-density electrical method measuring system for monitoring landslides, characterized by, The system comprises: a collection unit arranged in a to-be-monitored area of a mountain slope, the collection unit being configured to collect apparent resistivity of each monitoring point in the to-be-monitored area; a measurement terminal electrically connected to the collection unit, configured to receive the apparent resistivity collected by the collection unit; a communication unit wirelessly connected to the measurement terminal; a cloud server wirelessly connected to the communication unit, the measurement terminal being configured to transmit the received apparent resistivity to the cloud server through the communication unit; wherein the cloud server comprises: a data processing unit configured to obtain monitoring data of the to-be-monitored area after preprocessing the apparent resistivity, obtain apparent resistivity change rates of each monitoring point based on the monitoring data, obtain an apparent resistivity contour section map based on the apparent resistivity change rates, and determine a sensitivity of soil at each monitoring point position to rainfall reaction according to the apparent resistivity contour section map; the data processing unit comprises: The apparent resistivity rate of change determination module is configured to calculate the apparent resistivity rate of change p of each of the monitoring points δ The apparent resistivity rate of change p δ is calculated according to the following formula: wherein ρ δ is the apparent resistivity rate of change, ρ0 is the apparent resistivity reference value, ρ i is the apparent resistivity value of the i-th measurement, and Δρ is the difference between the apparent resistivity value of the i-th measurement and the apparent resistivity reference value; The time curve graph establishing module is configured to establish a time curve graph of the apparent resistivity change rate p of each monitoring point according to the apparent resistivity change rate p of each monitoring point δ The time curve graph establishing module is configured to establish a time curve graph of the apparent resistivity change rate p of each monitoring point according to the apparent resistivity change rate p of each monitoring point δ The time curve graph establishing module is configured to establish a time curve graph of the apparent resistivity change rate p of each monitoring point according to a processing module configured to determine information about changes of soil resistivity with soil water content according to a time sequence curve slope of the time sequence curve graph.

2. The high-density electrical method measuring system for monitoring landslides according to claim 1, characterized in that, The data processing unit further comprises: a monitoring data preprocessing module configured to obtain gridded data after preprocessing the apparent resistivity after obtaining the apparent resistivity, and determine the monitoring data based on the gridded data.

3. The high-density electrical method measuring system for monitoring landslides according to claim 2, characterized in that, The monitoring data preprocessing module comprises: a distortion point processing module configured to obtain first data after eliminating distortion point data in the apparent resistivity; a gridding processing module configured to obtain the gridded data after gridding processing of the first data by using a triangular net interpolation method.

4. The high-density electrical method measuring system for monitoring landslides according to claim 1, characterized in that, The processing module is further configured to calculate the time sequence curve slope k of the time sequence curve graph according to the following formula: k=tanα wherein k is the time sequence curve slope, and α is an included angle between a tangent of a point in the time sequence curve graph and an x-axis.

5. The high-density electrical measurement system for mountain landslide monitoring according to claim 4, wherein the processing module is further configured to, when determining the information about changes of soil resistivity with soil water content according to the time sequence curve slope of the time sequence curve graph, include: when α is an obtuse angle, k<0, the time sequence curve slope is negative, it is judged that rainfall causes an increase of soil water content and a decrease of apparent resistivity value; when α is an acute angle, k>0, the time sequence curve slope is positive, it is judged that drought causes a decrease of soil water content and an increase of apparent resistivity value.

6. The high-density electrical method measuring system for monitoring landslides according to claim 1, characterized in that, The data processing unit further comprises: an isopleth section map establishing module configured to establish an apparent resistivity isopleth section map according to the apparent resistivity change rates, display a distribution condition of apparent resistivity of the entire monitoring section and display electrical characteristics of low-resistance and high-resistance distribution based on the apparent resistivity isopleth section map; wherein the apparent resistivity isopleth section map contains distribution characteristics of the apparent resistivity change rates in horizontal and vertical directions, and the apparent resistivity isopleth section map is arranged in time sequence.

7. The high-density electrical measurement system for mountain landslide monitoring according to claim 6, wherein The contour profile map establishing module is further configured to include the following when establishing the apparent resistivity contour profile map: The absolute value of the difference between the maximum and minimum apparent resistivity change rates of each monitoring point in a monitoring period is selected as the maximum change amount for drawing the map.

8. The high-density electrical method measuring system for monitoring landslides according to claim 1, characterized in that, The time series curve map establishing module extracts specific and regular monitoring point data in the vertical direction and the horizontal direction for drawing the apparent resistivity change rate time series curve map when establishing the apparent resistivity change rate time series curve map.

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